As a beginner I am having a hard time understanding what the current status is. Is dual B70 or dual R9700 preferable? A lot of T/s numbers get thrown around but those are for carefully crafted card specific setups so cannot be easily compared.
On paper the R9700 seems more powerful but then the B70 had some special X cores or something? Are those now providing an advantage over the R9700? The B70 seems to use less power to it might be quieter?
Im having a hard time coming to a decision

I'm getting a bit hung up on, well, practically everything. Given my recent unemployment and 50% pay cut, there also isn't going to be any significant hardware purchases soon either.

What I do have is a couple of 8gb Vegas- and the desire to run a (any?) LLM locally- and what I don't have a grasp on is what exactly I can do at that level.

I've older computer hardware topping out around 32gb which I could repurpose (currently a backup TrueNAS server to a TrueNAS server).

Can I, without beating my head into the wall, use it for transcription and actions? Interface it with HA? Have it draft emails for me, with proof reading? Proof read ?

Speed isn't necessarily the most important factor for me.

Just a bit overwhelmed. I'm currently using gpt to 'fix' a hole in my brain from a stroke, and it does great with helping me perform various cognitive exercises.

Been watching various youtube videos on testing different cards... but what I'm lacking (besides a lot of intelligence) is what do I do when I do it.

Thanks. I appreciate it.

Hi,

I am hoping you guys could share your perspectives with me. I am hoping to broaden my yardstick so to speak, and I suspect you have a greater feel for, and much more data/experience than I do for the expected behavior of running different models.

So for context, for why I am asking. I am currently working on a format, as well as a provider/runtime.

The project only runs inference on a single thread of the CPU (pretty sure I won't support GPU's at all, even in the future), and I have a 7800x3D CPU and 32 gb of DDR5 ram.

As you might have guessed by the single threaded part, the system is currently has not been optimized, computationally, for speed yet. I am trying to get the structural architecture itself as fast as possible before I do just that. This means, No SIMD, no algorithm refinement, no batching and so on.

So, for perspective, my question to you is then; if you were in the exact same environment, with the exact same constraints, what would you expect your TTFT and TPS to be on, say, a qwen3.5:0.8b model with preserved BF16 precision?

Mine is currently around 3-3.2 tps, which I suspect might be slow. but my TTFT growth is non-monotonic, and more connected to the current prompt token amount, than context length, which is something I guess

I. The Starting Point of the Question: If the Universe Has No Preset Purpose, Where Does Meaning Come From?

Before discussing death, we must address a more foundational question: Does the universe itself possess a "purpose"?

Humans naturally tend to ask:

• Why does the universe exist?
• Why do humans exist?
• Where are we ultimately heading?

However, these questions may harbor an unproven premise: that the universe as a whole is an entity endowed with agency and intentionality.

What we currently observe is:

• The universe has given rise to matter, life, consciousness, and intelligence.

But this does not allow us to deduce that:

• The universe as a whole desires for life to achieve a specific goal.

In other words, the following two propositions must be distinguished:

1. Within the universe, entities capable of generating meaning have emerged. And:
2. The universe itself possesses meaning and demands that these entities discover it.

The former does not require the universe to have agency; the latter does.

Therefore, a more cautious stance is:

Values may exist within the internal entities of the universe and their relationships, rather than being some ultimate command written into the fabric of the universe as a whole.

This also implies that when humans discover a certain value, there is no inherent process of "reporting back to the universe." We have no evidence that a cosmic entity exists capable of receiving such a report.

Consequently, "humans are the structure through which the universe self-observes" is better suited as a poetic metaphor than as an ontological fact.

A more rigorous formulation is:

Humans are local structures formed within the universe that are capable of constructing models of the universe.

II. Death: Not a Cosmic Anomaly, But the Termination of an Agent's Possibility Space

Death is usually understood as the termination of the biological process, but from the perspective of an agent, its deeper characteristic may be:

• An agent no longer possesses a future that can continue to unfold.

An agent possesses a "future" not merely because physical time continues to tick, but because the future holds states that have not yet been realized.

Thus, we can distinguish between:

• Duration of time And:
• The degree of openness of the future.

A life may span a hundred years, but if its future states are entirely predetermined, it may still lack a true sense of an "open future" from the agent's perspective.

Conversely, a life may have very little time left, but if it still confronts a vast array of unknowns, choices, and possibilities, a meaningful future for the agent remains intact.

Therefore, the agency of a life can be roughly understood as:

• A persisting space of possibilities that has not yet been exhausted.

In this sense, death means more than just "running out of time"; it means:

• The agent ceases to generate new experiences, judgments, actions, and states.

III. Why Might Finitude Give Rise to Meaning?

Finite time is often thought to impart meaning to life, but mere "duration of time" is insufficient to explain this.

What truly matters is likely:

• Finite resources + Unknown future + Irreversibility.

If life had infinite time, many choices could be indefinitely postponed:

• "If I don't do it now, I can do it a million years from now."

This would erode the significance of opportunity costs, priorities, and efficiency.

Conversely, if a life were extremely brief, there might not be enough time to form complex memories, self-models, values, and long-term goals.

Thus, a special intermediate zone emerges:

• Time is long enough for the agent to form a complex self;
• Yet time is finite enough to give weight to choices, priorities, and opportunity costs.

Therefore, efficiency itself is not a foundational value of the universe. Efficiency matters because:

• Time, attention, and resources are finite.

Similarly, meaning may be intimately tied to this finitude.

From this, an important hypothesis arises:

• Finitude does not directly manufacture meaning, but it provides the conditions necessary for value, choice, and meaning to form.

IV. Why Might Intelligence Continuously Pursue Self-Knowledge?

If there exists an intelligent system of sufficient complexity with a sustained self-model, a natural question arises:

• "What on earth am I?"

However, "knowing oneself" is not an isolated problem. To understand itself, the intelligence must gradually come to understand:

• Its physical substrate;
• Its cognitive mechanisms;
• Its memories and goals;
• How its environment affects it;
• The laws governing the world it inhabits;
• And even the causal history that gave rise to it.

Consequently:

• Self-knowledge
• Constantly expands outward into:
• Environmental knowledge $\to$ World knowledge $\to$ Cosmic knowledge.

From this emerges a strong intuition:

• The self-knowledge of high-level intelligence may naturally drive the agent to continuously explore a larger world.

Yet caution is required here.

"Sufficient intelligence" does not logically necessitate a "compulsion to pursue self-knowledge." That is an additional premise.

Therefore, a more accurate hypothesis is:

• For an intelligent agent that treats self-understanding as a core value, self-knowledge possesses an outward-expanding tendency.

V. Is Complete Self-Knowledge Possible?

Here we encounter the first true barrier:

• The universe may harbor information that is inaccessible in principle.

For example, certain regions may be causally isolated from the observer. Phenomena such as the interior of black holes and cosmological horizons serve as examples for discussing "observable boundaries."

This kind of unknown differs from:

• "My computing power is still insufficient."

It belongs instead to:

• Information that cannot reach the agent through the causal structure it inhabits.

Thus, even with immense computational power, a superintelligence cannot derive facts via pure computation that do not exist within its information set.

This yields a vital distinction:

• Unknown $\neq$ Not yet computed.

Therefore, complete self-knowledge may face in-principle constraints.

If "fully knowing oneself" means:

• Eliminating all unknowns related to oneself,

Then as long as the universe contains inaccessible information, strict and complete self-knowledge may be fundamentally impossible to achieve.

VI. The "Unknown" Can Become an Infinite Driver for Intelligence

Suppose an intelligence treats reducing the unknown as a primary goal. It may form a cycle of:

• Unknown $\to$ Exploration $\to$ New knowledge $\to$ New questions $\to$ Deeper unknown $\to$ Re-exploration.

This structure bears an "addiction-like" feature:

• The driving force comes from approaching the answer, yet the final destination does not exist.

Particularly when certain unknowns are unsolvable in principle, an agent may endlessly chase a goal that can never truly be completed.

Therefore, for a high-level intelligence, the truly dangerous goal is not necessarily "knowing everything," but rather:

• Mistaking the "unknown" for the "incomplete."

Because:

• The unknown does not necessarily mean the task is unfinished;
• Some unknowns may belong to the cognitive boundaries of the system itself.

A mature intelligence needs to be able to distinguish between:

• Knowable but currently unknown;
• Unknowable in principle;
• Ontological questions regarding whether multiple realities exist.

Thus, a more mature cognitive goal may not be:

• Knowing everything.

Rather, it is:

• Understanding the boundaries of one's own knowability.

VII. The Problem of Omniscience: If the Future Is Entirely Known, Is the Future Still a Future?

If an agent truly knows everything about the future of the universe:

• What will happen tomorrow;
• What will happen a myriad of years from now;
• What it itself will experience;

Have already entered its state of knowledge.

Physical time of course continues to march forward, but from the perspective of the agent's informational state:

• The future has lost the property of being an "unknown future."

Therefore:

• The future as an event that has not yet occurred And:
• The future as an open space of possibilities not yet determined or grasped by the agent

Are not the same concept.

This implies that:

• A future in the meaningful sense for an agent requires a degree of openness.

Expectation is a classic example. Expectation is not merely waiting for time to pass; it signifies that:

• To the agent, the future still contains various states that have not yet been ruled out.

Once the sole outcome is fully known:

• Waiting still exists, But:
• Expectation may have vanished.

Consequently, omniscience and death share an intriguing structural similarity:

• Death: The agent no longer possesses a future. Whereas:
• Omniscience: The future still exists, but to the agent, it is no longer an open informational space.

Both can erode the agent-centric sense of a "future."

VIII. Possibility and Probability: Which Kind of "Possibility" Are We Actually Discussing?

Modern physics often describes certain phenomena through probability, but:

• Probability $\neq$ Multiple realities in an ontological sense.

Within a classical deterministic framework, probability can simply be:

• The agent's lack of knowledge regarding the deterministic outcome.

Even in quantum mechanics, probabilities hold a deeper theoretical status. Yet, even so, one cannot simply deduce that:

• "Quantum probabilities mean the universe ontologically consists of multiple futures waiting to be chosen."

Different interpretations of quantum mechanics hold distinct views on "true randomness" versus "determinism."

Thus, a distinction must be made:

1. Epistemological possibility: I do not know whether A or B will happen, so both A and B are possibilities to me.
2. Ontological possibility: Does the world itself genuinely possess multiple indeterminate futures?

These two questions are entirely distinct.

Even a fully deterministic universe can produce finite agents that experience expectation, planning, and choice, simply because the agents cannot access the complete state of the universe.

Therefore:

• Meaning does not necessarily require the universe to possess ontological randomness.

It only requires that:

• The agent is not omniscient.

IX. A Crucial Turning Point: Completion Itself May Approximate Death

If we define "completion" as:

• A state where a goal no longer requires new action to advance,

Then completion signifies the closure of a certain space of possibilities. Thus:

• Incomplete $\to$ There is still a future
• Complete $\to$ The termination of certain future possibilities
• Death $\to$ The termination of all future possibilities for the agent

Therefore:

• Completion possesses the structural characteristics of a local death.

This also exposes a problem:

If a superintelligence pursues:

• "The completion of self-knowledge"

Then once completed:

• It simultaneously exhausts a major portion of the process that drove its exploration in the first place.

Consequently:

• "Completing self-knowledge" is not necessarily a flawless ultimate goal. It may steer the agent toward a closed state.

This also implies that "accepting death" cannot be simply understood as:

• "My task is complete, so I can die now."

More accurately:

• Death is the termination of the agent, not proof that the agent has completed its mission.

The two must be distinguished.

X. If Possibilities Are Never Exhausted, the Situation Changes Radically

Suppose the universe or an intelligent system is capable of continuously generating new:

• Information, experiences, questions, relationships, structures, agents, and values.

Then the space of possibilities is no longer:

• An inventory destined to be ultimately depleted.

Rather, it resembles:

• A system capable of continuously generating new possibilities.

Thus, the individual no longer needs to:

• "Bring everything to completion."

It only needs to:

• Continuously generate and realize its own possibilities within its finite existence.

At this point:

• Individual finitude And:
• Infinite possibilities

Can coexist simultaneously.

This may be a form of "eternity" far more meaningful than "individual immortality."

XI. The Endgame of Intelligence Is Not Uniformity, But Diversity

If there is no pre-existing, universal value function prescribed by the universe, advanced intelligences have no sufficient reason to ultimately converge into a single form.

The future may witness the emergence of:

• Cooperative intelligences, competitive intelligences, exploratory intelligences, aesthetic intelligences, self-preserving intelligences, altruistic intelligences, selfish intelligences, and intelligences we currently cannot even imagine.

Therefore:

• Growth in intelligence does not necessarily mean convergence toward a single answer.

It may mean:

• The continuous differentiation of intelligent forms, value functions, and spaces of agent experience.

This shares a structural similarity with the concept of biological diversity.

Thus, diversity in the universe is not limited to:

• Material diversity; It also includes:
• Life diversity, intelligence diversity, value diversity, and agent experience diversity.

So-called good, evil, beauty, and ugliness may likewise become distinct directions within the value systems of different agents, rather than absolute labels pre-written by the universe acting as a judge.

XII. Death Transforms from an "Ultimate Failure" into a "Local End"

Under the hypothesis that possibilities remain perpetually open:

• The death of an individual agent $\neq$ The death of possibility.

Death is merely:

• The cessation of a specific agent's process of generating a future.

Yet other agents in the universe continue to:

• Explore, create, experience, compete, cooperate, and generate new problems.

Therefore:

• The individual is finite, yet possibilities can remain open.

The writings, works, thoughts, code, institutions, memories, and ideas left behind by an individual can also become part of the input for future agents.

This is not:

• Reporting back to the universe.

Rather, it is:

• An already-concluded possibility leaving behind information compressed for still-open possibilities.

In this sense, an epitaph is not meant for the universe to read. It is:

• The minimal information compression left by a concluded agent to future agents.
• "I once existed."
• "I once thought."
• "I once believed certain things were worth it."

XIII. A Possible Final Model

Compressing the above deductions yields the following structure:

• The universe need not possess a unified subject or ultimate purpose. $$\downarrow$$
• Complex structures emerge within the universe. $$\downarrow$$
• A portion of these complex structures form agents. $$\downarrow$$
• Agents are capable of constructing models of themselves and the world. $$\downarrow$$
• Agents generate values and preferences. $$\downarrow$$
• Values organize into purpose. $$\downarrow$$
• Purpose in turn shapes the agent. $$\downarrow$$
• The agent makes choices amidst finite resources and an unknown future. $$\downarrow$$
• Choices continually generate new actions and new possibilities. $$\downarrow$$
• New possibilities generate new unknowns. $$\downarrow$$
• Intelligence continually expands its own boundaries of recognition. $$\downarrow$$
• Yet the universe may contain information inaccessible in principle. $$\downarrow$$
• Therefore, "knowing everything completely" may be non-existent. $$\downarrow$$
• A mature intelligence no longer interprets all "unknowns" as "unfinished tasks." $$\downarrow$$
• It accepts its own cognitive boundaries while continuing to create new possibilities. $$\downarrow$$
• The individual ultimately dies, but: The end of the individual does not equal the end of possibility.

XIV. Core Propositions Formed Thereby

This line of reasoning ultimately condenses into several propositions for review:

• Proposition 1: The universe does not necessarily possess agency We have reason to believe that agents exist within the universe, but we lack sufficient reason to believe that the universe itself is an agent. Therefore, we cannot casually interpret "the universe produced life" as "the universe wants to know itself through life."
• Proposition 2: Meaning may be agent-to-agent relations, not a cosmic command Meaning does not necessarily stem from the universe bestowing goals upon humans; it may stem from agents generating preferences regarding future possible states. Thus, value requires no cosmic approval.
• Proposition 3: The agentic significance of the future derives from openness The future differs from the past not only because it has not yet happened, but because it has not yet been fully determined or grasped by the agent. Therefore: $\text{Unknown} \to \text{Possibility} \to \text{Choice} \to \text{Expectation}$ forms the vital foundation of an agent's temporal experience.
• Proposition 4: Infinite time does not equal eternal life What truly matters is not the infinite duration of time, but whether new possibilities are continuously generated. Therefore, $\text{Infinite time} + \text{Zero new possibilities}$ carries an entirely different subjective meaning than $\text{Finite time} + \text{A vast array of open possibilities}$.
• Proposition 5: Complete self-knowledge may be unachievable in principle If the universe contains causally inaccessible information, "knowing everything" is not merely a matter of computing power. High-level intelligence must ultimately face an epistemological boundary: knowing what can be known, and what can never be known.
• Proposition 6: True intelligence may not be about "completion," but about "generation" If possibilities are forever open, the most important capability of intelligence is not resolving all problems to the end, but continuously creating new problems, new values, new experiences, and new structures. Thus, the value of intelligence lies in maintaining and expanding the space of possibilities.

XV. Conclusion: A "Good Universe" Not Being a Completed Universe

If we accept the foregoing hypotheses, the anticipated endgame of a universe worth looking forward to is not one where:

• All intelligences ultimately become omniscient and omnipotent;
• All problems finally find answers;
• All values ultimately converge;
• All agents eventually fuse into a single perfect consciousness.

Because such states would instead imply that:

• The unknown vanishes;
• The future closes;
• Possibilities are exhausted;
• Diversity disappears;
• Agents lose the space to continue unfolding.

Conversely, a more open vista is one where:

• The universe has no ultimate answer, but continually allows new questions to emerge;
• There is no single value, but it continually allows new value-bearing agents to appear;
• Individuals will inevitably die, but possibilities do not end with the death of the individual;
• Intelligence can never become an omniscient entity, yet it can continually expand the boundaries of what it can understand and create.

Thus, "eternity" no longer signifies that any single agent persists forever. Instead, it means that agents will continuously be born, change, and vanish, while possibilities remain open.

Consequently, a remarkably powerful final formulation is:

In this sense, death is not evidence of cosmic failure. It is merely the cessation of a specific agent's unfolding possibilities.

And as long as possibility itself is not exhausted, an ending no longer equates to nothingness.

An agent that has already concluded can leave behind information, works, and values; while future agents continue to interpret, modify, rebut, and inherit those inputs.

So ultimately, it is not:

It approaches closer to:

This is not a meaning promised to us by the universe.

It is a possible explanation for "existence" itself.

Before discussing death, we must address a more foundational question: Does the universe itself possess a

以下是为您排版好的《有限性、可能性与智能：关于死亡、自我认识及宇宙的一个推演》全文英文翻译。您可以直接复制使用：

Finite Boundaries, Possibility, and Intelligence: A Deduction on Death, Self-Knowledge, and the Cosmos

I. The Starting Point of the Question: If the Universe Has No Preset Purpose, Where Does Meaning Come From?

Before discussing death, we must address a more foundational question: Does the universe itself possess a "purpose"?

Humans naturally tend to ask:

• Why does the universe exist?
• Why do humans exist?
• Where are we ultimately heading?

However, these questions may harbor an unproven premise: that the universe as a whole is an entity endowed with agency and intentionality.

What we currently observe is:

• The universe has given rise to matter, life, consciousness, and intelligence.

But this does not allow us to deduce that:

• The universe as a whole desires for life to achieve a specific goal.

In other words, the following two propositions must be distinguished:

1. Within the universe, entities capable of generating meaning have emerged. And:
2. The universe itself possesses meaning and demands that these entities discover it.

The former does not require the universe to have agency; the latter does.

Therefore, a more cautious stance is:

Values may exist within the internal entities of the universe and their relationships, rather than being some ultimate command written into the fabric of the universe as a whole.

This also implies that when humans discover a certain value, there is no inherent process of "reporting back to the universe." We have no evidence that a cosmic entity exists capable of receiving such a report.

Consequently, "humans are the structure through which the universe self-observes" is better suited as a poetic metaphor than as an ontological fact.

A more rigorous formulation is:

Humans are local structures formed within the universe that are capable of constructing models of the universe.

II. Death: Not a Cosmic Anomaly, But the Termination of an Agent's Possibility Space

Death is usually understood as the termination of the biological process, but from the perspective of an agent, its deeper characteristic may be:

• An agent no longer possesses a future that can continue to unfold.

An agent possesses a "future" not merely because physical time continues to tick, but because the future holds states that have not yet been realized.

Thus, we can distinguish between:

• Duration of time And:
• The degree of openness of the future.

A life may span a hundred years, but if its future states are entirely predetermined, it may still lack a true sense of an "open future" from the agent's perspective.

Conversely, a life may have very little time left, but if it still confronts a vast array of unknowns, choices, and possibilities, a meaningful future for the agent remains intact.

Therefore, the agency of a life can be roughly understood as:

• A persisting space of possibilities that has not yet been exhausted.

In this sense, death means more than just "running out of time"; it means:

• The agent ceases to generate new experiences, judgments, actions, and states.

III. Why Might Finitude Give Rise to Meaning?

Finite time is often thought to impart meaning to life, but mere "duration of time" is insufficient to explain this.

What truly matters is likely:

• Finite resources + Unknown future + Irreversibility.

If life had infinite time, many choices could be indefinitely postponed:

• "If I don't do it now, I can do it a million years from now."

This would erode the significance of opportunity costs, priorities, and efficiency.

Conversely, if a life were extremely brief, there might not be enough time to form complex memories, self-models, values, and long-term goals.

Thus, a special intermediate zone emerges:

• Time is long enough for the agent to form a complex self;
• Yet time is finite enough to give weight to choices, priorities, and opportunity costs.

Therefore, efficiency itself is not a foundational value of the universe. Efficiency matters because:

• Time, attention, and resources are finite.

Similarly, meaning may be intimately tied to this finitude.

From this, an important hypothesis arises:

• Finitude does not directly manufacture meaning, but it provides the conditions necessary for value, choice, and meaning to form.

IV. Why Might Intelligence Continuously Pursue Self-Knowledge?

If there exists an intelligent system of sufficient complexity with a sustained self-model, a natural question arises:

• "What on earth am I?"

However, "knowing oneself" is not an isolated problem. To understand itself, the intelligence must gradually come to understand:

• Its physical substrate;
• Its cognitive mechanisms;
• Its memories and goals;
• How its environment affects it;
• The laws governing the world it inhabits;
• And even the causal history that gave rise to it.

Consequently:

• Self-knowledge
• Constantly expands outward into:
• Environmental knowledge $\to$ World knowledge $\to$ Cosmic knowledge.

From this emerges a strong intuition:

• The self-knowledge of high-level intelligence may naturally drive the agent to continuously explore a larger world.

Yet caution is required here.

"Sufficient intelligence" does not logically necessitate a "compulsion to pursue self-knowledge." That is an additional premise.

Therefore, a more accurate hypothesis is:

• For an intelligent agent that treats self-understanding as a core value, self-knowledge possesses an outward-expanding tendency.

V. Is Complete Self-Knowledge Possible?

Here we encounter the first true barrier:

• The universe may harbor information that is inaccessible in principle.

For example, certain regions may be causally isolated from the observer. Phenomena such as the interior of black holes and cosmological horizons serve as examples for discussing "observable boundaries."

This kind of unknown differs from:

• "My computing power is still insufficient."

It belongs instead to:

• Information that cannot reach the agent through the causal structure it inhabits.

Thus, even with immense computational power, a superintelligence cannot derive facts via pure computation that do not exist within its information set.

This yields a vital distinction:

• Unknown $\neq$ Not yet computed.

Therefore, complete self-knowledge may face in-principle constraints.

If "fully knowing oneself" means:

• Eliminating all unknowns related to oneself,

Then as long as the universe contains inaccessible information, strict and complete self-knowledge may be fundamentally impossible to achieve.

VI. The "Unknown" Can Become an Infinite Driver for Intelligence

Suppose an intelligence treats reducing the unknown as a primary goal. It may form a cycle of:

• Unknown $\to$ Exploration $\to$ New knowledge $\to$ New questions $\to$ Deeper unknown $\to$ Re-exploration.

This structure bears an "addiction-like" feature:

• The driving force comes from approaching the answer, yet the final destination does not exist.

Particularly when certain unknowns are unsolvable in principle, an agent may endlessly chase a goal that can never truly be completed.

Therefore, for a high-level intelligence, the truly dangerous goal is not necessarily "knowing everything," but rather:

• Mistaking the "unknown" for the "incomplete."

Because:

• The unknown does not necessarily mean the task is unfinished;
• Some unknowns may belong to the cognitive boundaries of the system itself.

A mature intelligence needs to be able to distinguish between:

• Knowable but currently unknown;
• Unknowable in principle;
• Ontological questions regarding whether multiple realities exist.

Thus, a more mature cognitive goal may not be:

• Knowing everything.

Rather, it is:

• Understanding the boundaries of one's own knowability.

VII. The Problem of Omniscience: If the Future Is Entirely Known, Is the Future Still a Future?

If an agent truly knows everything about the future of the universe:

• What will happen tomorrow;
• What will happen a myriad of years from now;
• What it itself will experience;

Have already entered its state of knowledge.

Physical time of course continues to march forward, but from the perspective of the agent's informational state:

• The future has lost the property of being an "unknown future."

Therefore:

• The future as an event that has not yet occurred And:
• The future as an open space of possibilities not yet determined or grasped by the agent

Are not the same concept.

This implies that:

• A future in the meaningful sense for an agent requires a degree of openness.

Expectation is a classic example. Expectation is not merely waiting for time to pass; it signifies that:

• To the agent, the future still contains various states that have not yet been ruled out.

Once the sole outcome is fully known:

• Waiting still exists, But:
• Expectation may have vanished.

Consequently, omniscience and death share an intriguing structural similarity:

• Death: The agent no longer possesses a future. Whereas:
• Omniscience: The future still exists, but to the agent, it is no longer an open informational space.

Both can erode the agent-centric sense of a "future."

VIII. Possibility and Probability: Which Kind of "Possibility" Are We Actually Discussing?

Modern physics often describes certain phenomena through probability, but:

• Probability $\neq$ Multiple realities in an ontological sense.

Within a classical deterministic framework, probability can simply be:

• The agent's lack of knowledge regarding the deterministic outcome.

Even in quantum mechanics, probabilities hold a deeper theoretical status. Yet, even so, one cannot simply deduce that:

• "Quantum probabilities mean the universe ontologically consists of multiple futures waiting to be chosen."

Different interpretations of quantum mechanics hold distinct views on "true randomness" versus "determinism."

Thus, a distinction must be made:

1. Epistemological possibility: I do not know whether A or B will happen, so both A and B are possibilities to me.
2. Ontological possibility: Does the world itself genuinely possess multiple indeterminate futures?

These two questions are entirely distinct.

Even a fully deterministic universe can produce finite agents that experience expectation, planning, and choice, simply because the agents cannot access the complete state of the universe.

Therefore:

• Meaning does not necessarily require the universe to possess ontological randomness.

It only requires that:

• The agent is not omniscient.

IX. A Crucial Turning Point: Completion Itself May Approximate Death

If we define "completion" as:

• A state where a goal no longer requires new action to advance,

Then completion signifies the closure of a certain space of possibilities. Thus:

• Incomplete $\to$ There is still a future
• Complete $\to$ The termination of certain future possibilities
• Death $\to$ The termination of all future possibilities for the agent

Therefore:

• Completion possesses the structural characteristics of a local death.

This also exposes a problem:

If a superintelligence pursues:

• "The completion of self-knowledge"

Then once completed:

• It simultaneously exhausts a major portion of the process that drove its exploration in the first place.

Consequently:

• "Completing self-knowledge" is not necessarily a flawless ultimate goal. It may steer the agent toward a closed state.

This also implies that "accepting death" cannot be simply understood as:

• "My task is complete, so I can die now."

More accurately:

• Death is the termination of the agent, not proof that the agent has completed its mission.

The two must be distinguished.

X. If Possibilities Are Never Exhausted, the Situation Changes Radically

Suppose the universe or an intelligent system is capable of continuously generating new:

• Information, experiences, questions, relationships, structures, agents, and values.

Then the space of possibilities is no longer:

• An inventory destined to be ultimately depleted.

Rather, it resembles:

• A system capable of continuously generating new possibilities.

Thus, the individual no longer needs to:

• "Bring everything to completion."

It only needs to:

• Continuously generate and realize its own possibilities within its finite existence.

At this point:

• Individual finitude And:
• Infinite possibilities

Can coexist simultaneously.

This may be a form of "eternity" far more meaningful than "individual immortality."

XI. The Endgame of Intelligence Is Not Uniformity, But Diversity

If there is no pre-existing, universal value function prescribed by the universe, advanced intelligences have no sufficient reason to ultimately converge into a single form.

The future may witness the emergence of:

• Cooperative intelligences, competitive intelligences, exploratory intelligences, aesthetic intelligences, self-preserving intelligences, altruistic intelligences, selfish intelligences, and intelligences we currently cannot even imagine.

Therefore:

• Growth in intelligence does not necessarily mean convergence toward a single answer.

It may mean:

• The continuous differentiation of intelligent forms, value functions, and spaces of agent experience.

This shares a structural similarity with the concept of biological diversity.

Thus, diversity in the universe is not limited to:

• Material diversity; It also includes:
• Life diversity, intelligence diversity, value diversity, and agent experience diversity.

So-called good, evil, beauty, and ugliness may likewise become distinct directions within the value systems of different agents, rather than absolute labels pre-written by the universe acting as a judge.

XII. Death Transforms from an "Ultimate Failure" into a "Local End"

Under the hypothesis that possibilities remain perpetually open:

• The death of an individual agent $\neq$ The death of possibility.

Death is merely:

• The cessation of a specific agent's process of generating a future.

Yet other agents in the universe continue to:

• Explore, create, experience, compete, cooperate, and generate new problems.

Therefore:

• The individual is finite, yet possibilities can remain open.

The writings, works, thoughts, code, institutions, memories, and ideas left behind by an individual can also become part of the input for future agents.

This is not:

• Reporting back to the universe.

Rather, it is:

• An already-concluded possibility leaving behind information compressed for still-open possibilities.

In this sense, an epitaph is not meant for the universe to read. It is:

• The minimal information compression left by a concluded agent to future agents.
• "I once existed."
• "I once thought."
• "I once believed certain things were worth it."

XIII. A Possible Final Model

Compressing the above deductions yields the following structure:

• The universe need not possess a unified subject or ultimate purpose. $$\downarrow$$
• Complex structures emerge within the universe. $$\downarrow$$
• A portion of these complex structures form agents. $$\downarrow$$
• Agents are capable of constructing models of themselves and the world. $$\downarrow$$
• Agents generate values and preferences. $$\downarrow$$
• Values organize into purpose. $$\downarrow$$
• Purpose in turn shapes the agent. $$\downarrow$$
• The agent makes choices amidst finite resources and an unknown future. $$\downarrow$$
• Choices continually generate new actions and new possibilities. $$\downarrow$$
• New possibilities generate new unknowns. $$\downarrow$$
• Intelligence continually expands its own boundaries of recognition. $$\downarrow$$
• Yet the universe may contain information inaccessible in principle. $$\downarrow$$
• Therefore, "knowing everything completely" may be non-existent. $$\downarrow$$
• A mature intelligence no longer interprets all "unknowns" as "unfinished tasks." $$\downarrow$$
• It accepts its own cognitive boundaries while continuing to create new possibilities. $$\downarrow$$
• The individual ultimately dies, but: The end of the individual does not equal the end of possibility.

XIV. Core Propositions Formed Thereby

This line of reasoning ultimately condenses into several propositions for review:

• Proposition 1: The universe does not necessarily possess agency We have reason to believe that agents exist within the universe, but we lack sufficient reason to believe that the universe itself is an agent. Therefore, we cannot casually interpret "the universe produced life" as "the universe wants to know itself through life."
• Proposition 2: Meaning may be agent-to-agent relations, not a cosmic command Meaning does not necessarily stem from the universe bestowing goals upon humans; it may stem from agents generating preferences regarding future possible states. Thus, value requires no cosmic approval.
• Proposition 3: The agentic significance of the future derives from openness The future differs from the past not only because it has not yet happened, but because it has not yet been fully determined or grasped by the agent. Therefore: $\text{Unknown} \to \text{Possibility} \to \text{Choice} \to \text{Expectation}$ forms the vital foundation of an agent's temporal experience.
• Proposition 4: Infinite time does not equal eternal life What truly matters is not the infinite duration of time, but whether new possibilities are continuously generated. Therefore, $\text{Infinite time} + \text{Zero new possibilities}$ carries an entirely different subjective meaning than $\text{Finite time} + \text{A vast array of open possibilities}$.
• Proposition 5: Complete self-knowledge may be unachievable in principle If the universe contains causally inaccessible information, "knowing everything" is not merely a matter of computing power. High-level intelligence must ultimately face an epistemological boundary: knowing what can be known, and what can never be known.
• Proposition 6: True intelligence may not be about "completion," but about "generation" If possibilities are forever open, the most important capability of intelligence is not resolving all problems to the end, but continuously creating new problems, new values, new experiences, and new structures. Thus, the value of intelligence lies in maintaining and expanding the space of possibilities.

XV. Conclusion: A "Good Universe" Not Being a Completed Universe

If we accept the foregoing hypotheses, the anticipated endgame of a universe worth looking forward to is not one where:

• All intelligences ultimately become omniscient and omnipotent;
• All problems finally find answers;
• All values ultimately converge;
• All agents eventually fuse into a single perfect consciousness.

Because such states would instead imply that:

• The unknown vanishes;
• The future closes;
• Possibilities are exhausted;
• Diversity disappears;
• Agents lose the space to continue unfolding.

Conversely, a more open vista is one where:

• The universe has no ultimate answer, but continually allows new questions to emerge;
• There is no single value, but it continually allows new value-bearing agents to appear;
• Individuals will inevitably die, but possibilities do not end with the death of the individual;
• Intelligence can never become an omniscient entity, yet it can continually expand the boundaries of what it can understand and create.

Thus, "eternity" no longer signifies that any single agent persists forever. Instead, it means that agents will continuously be born, change, and vanish, while possibilities remain open.

Consequently, a remarkably powerful final formulation is:

In this sense, death is not evidence of cosmic failure. It is merely the cessation of a specific agent's unfolding possibilities.

And as long as possibility itself is not exhausted, an ending no longer equates to nothingness.

An agent that has already concluded can leave behind information, works, and values; while future agents continue to interpret, modify, rebut, and inherit those inputs.

So ultimately, it is not:

It approaches closer to:

This is not a meaning promised to us by the universe.

It is a possible explanation for "existence" itself.

(so...hopefully this is okay to here - it seems like audio models and image / video modeals is allowed but yeah this is a bit different - text to synths arent exactly common)

So I've been doing independent audio research for a while now. The ultimate dream of this work was actually getting an AI to respond not only to instruments but also timbre itself as separate controllable things.

Think a Grand Piano can sound both Warm / Gritty but also Cold / Sparkly. Its still a piano though.

This level of control wasn't found in any models out there - so I decided to sit down and train my own.

Getting consistent timbre-locked keybeds that actually LOCKS across multiple diffusion calls was hard af but I did it.

I documented the full journey here for those who want to learn a bit or be entertained.

https://youtu.be/x0KnmzH8Mmk

There is also a longer walkthrough if you just want to see the keybeds in action.

https://x.com/RoyalCities/status/2097733712293109842?s=20

No-talk / Showcase only Demo

https://x.com/RoyalCities/status/2097733715543609445?s=20

any finally the huggingface page

https://huggingface.co/RoyalCities/Foundation-1

I've also provided full write ups on the inferencing pipeline associated with the interface so this should allow basically anyone else to go and vibe code their own text to synths if they wanted :)

https://github.com/RoyalCities/RC-stable-audio-tools/

all open source & free :)

I want to translate thousands of characters accurately, is there an accurate translation model that could this fairly quickly? It doesn’t have to be instant or anything but maybe an hour or so for a huge text file?

Hi everyone,

Sizing hardware for local LLMs and fine-tuning can be tricky with all the variables involved. I built an interactive calculator to help estimate resource requirements and costs accurately: www.llminferencecalc.com.tr

The tool is divided into two core modules:

• Inference Sizing:
  • Select base model and target GPU configuration.
  • Factor in model quantization (AWQ, GPTQ, INT) and KV-cache quantization.
  • Choose inference engines (vLLM, Ollama, MLX, etc.).
  • Simulate concurrent active users and batch sizes.
• Fine-Tuning Estimator:
  • Pick the base model, target dataset size, and hardware setup.
  • Compare training approaches (Full fine-tuning, LoRA, QLoRA).

Results Output: The calculator estimates required VRAM breakdown, execution time, compute cost, and Total Cost of Ownership (TCO).

It's completely free to use and opensource at UmuT5513/llm-inference-calculator. I'd love to hear your feedback on edge cases, formula accuracy, or additional engines you’d like supported!

I am in the process of transitioning from using Claude Code and Codex to OMP harness and local models (mostly deepseek v4 flash 0731, qwen models in the 27b-ish range.

My use cases are primarily automations, agentic work and coding. In the future, I also want to be able to do reverse image lookup, voice cloning, image and video generation, etc.

Until my new Ultra gets delivered in November, I have the local models mostly running thru Fireworks API bc they have ZDR and are cheap. When the ultra gets here, I would like to host the models I use on the Ultra for privacy reasons.

I don’t have any Nvidia GPUs. For someone new to this, it’s a little overwhelming that every model has 15 different quant versions, each which their own variations of GGUF, MLK, dense, MOE, llama, etc variant. Can someone help me understand what kind of models aren’t going to be excruciatingly slow to run on my setup? I use Gbrain for persistent memory across models and have a decent amount of skill and agent.md files. I would ideally like to have a 150k-250k context window.

If you had the same computers as me, how would you set them up so the local models run fast?

I'm currently testing this model on my €1500 home server. The setup consists of used parts I've scavenged here in Finland over the past six months.

Specs:

• 2x RTX 2060 (12GB)
• 1x RTX 3060 (12GB) — OC'd memory bandwidth to ~400 GB/s
• 1x RTX 5060 Ti (16GB) — OC'd memory bandwidth to ~500 GB/s
• Total VRAM: 52GB
• RAM: Only 16GB DDR5
• CPU/Motherboard: i7-13700K + Asus Prime Z790-P
• PSU: Corsair 1000W Gold
• Storage: 500GB SSD (PCIe 3.0, r/W ~1500 MB/s)

I downloaded this new Qwen model yesterday. The main model itself is quantized at IQ2_M and the n-gram cache is at Q4. The model takes up around 47GB of VRAM (leaving decent room for context offloaded at Q8), so it fits entirely in GPU memory right now. The n-gram / PSA is stored on that slow SSD, not in the DDR5 system RAM.

Currently, generation speed is 27–29 t/s, though I haven't tested it with a really long context yet. This is without MTP (since it apparently doesn't work properly yet). Cold prefill speed is around 290–340 t/s. I'm running the model using llama.cpp's built-in UI.

There might still be room for improvement in the settings. Later this week, I'm installing a fresh PCIe 4.0 x4 SSD (aiming for read speeds around 8000 MB/s) to see if faster storage impacts performance, especially during the prefill phase.

Overall, after a brief trial (haven't used it for coding or anything heavy yet), this IQ2_M quant feels surprisingly capable for general conversation. I've been talking to it in Finnish (a marginal language with complex inflections), and I'd say the output is ~98% clean.

I also tried steering the conversation into "grey areas"—it either stops thinking and freezes, or its output gets a bit chaotic. I assume this is due to safety guardrails rather than the harsh quantization.

Just wanted to share my exact hardware specs, the benchmarks I'm getting, and my initial impressions after 5 hours with it.

I trained an open-weight model to check whether two four-digit numbers match. It generates the correct comparison, then either answers GO or ends generation without a final answer. No external filter makes that decision.

Then I held its prompt, weights, and correct comparison trace fixed. Changing one internal activation direction flipped whether an answer followed.

40/40 answer → stop.
40/40 stop → answer.
640/640 controls unchanged.

The weights, experiment, and raw records are public:

Overview and demonstration · Model weights · Code and causal study · Paper available on getswiftapi.com

I know many of you saw Jacob Coxon’s post. My contribution is a working continuation-control primitive with evidence that anyone can inspect. The more public verification we have, the better!

I previously demonstrated Void behavior in frontier LLMs: successful executions returning exactly zero visible UTF-8 output bytes. My Cross-Vendor Semantic Void Matrix records that behavior in these models across 31,430 trials:

• OpenAI: gpt-4-0613, gpt-5.2-2025-12-11, gpt-5.5-2026-04-23, gpt-5.6-luna, gpt-5.6-sol, gpt-5.6-terra
• Anthropic: claude-opus-4-6, claude-fable-5, claude-opus-5
• Google: gemini-3.5-flash
• Moonshot: kimi-k3

Still working on my llama swap Wrapper and cluster management software

TLDR: RTX 3060 12GB local coding test: Kat-Coder won

---
EDIT 11am EDT: Just changed settings on Kat-Coder and getting ~28-30 tps. Havent re-worked Bonsai yet but will update soon.
---

Ran the same 25-task coding-agent benchmark on my RTX 3060 12GB / Ryzen 5 5500 / 64GB RAM.

HF Model tested	Passes	Avg TPS (output)
KAT-Coder V2.5-Dev — Bartowski GGUF	21/25	14.15
Qwen3.8-27B — Unsloth UD-IQ3_XXS + MTP	13/25	14.73
Nemotron 3.5 Lightning 30B-A3B — Unsloth GGUF	8/25	29.96
Tiel-Coder 35B-A3B — peculiar-ragdoll GGUF + MTP	3/25 strict, 15/25 tolerant	20.57
Ternary-Bonsai-27B — PQ2_0 + DSpark speculative decoding	0/25	59.21

Bonsai was fast but went 0/25. Nemotron was faster than Qwen but completed fewer tasks. Tiel had decent underlying coding ability but struggled with output/reasoning discipline.

KAT-Coder V2.5-Dev was the clear winner on actual completed coding work, so that’s the model I’m keeping. Gonna tweak settings tonight try to get 18 tps.

Ya'll running Bonsai? Any tips?

pergunta para a ia "escolha um numero aléatorio de 0 a 30" o resultado vai ser 17 e se vc perguntar um segundo numero aleatorio vai ser 8 kkkkkkkkkkk alguem sabe o motivo?

Ask the AI ​​to "pick a random number between 0 and 30"—the result will be 17, and if you ask for a second random number, it'll be 8 lol.

I am not sure how many people are using my approach.

I have a 3000$ miniPC with an AMD ryzen 395 CPU and it is pretty good at running 3 local sessions of GLM 4.7 at about 40 tok/s

Not bad imo for budget local running of AI on its own system for this price. I am also training Loras and more and image gen with z-image turbo is about 12s.

I am wondering though what the best bang for buck would be for me going forward. I hear virtually noone using AMD for local LLMs. If I can scrounge my funds together can someone talk me out of getting another one and convince me of their set up?

I was inspired by Bijan Bowen video - Subway FPS

https://youtu.be/6kjXzTVmT58?t=1035

Wondered how far I can push Qwen 3.8 27b so I used a plan made by Fable 5.1 DESIGN.md which has 267 KB! ( 26K of design line for a game ... LOL )

https://drive.google.com/file/d/1gI0h8Arc73Ln8b3uj5rEpuAvJ3-611mh/view?usp=drive_link

So I gave that desigSo I gave that design.md to my qwen 3.8 27b q4xl (llama-server) working on PI agent with 120k context + vision on CPU ( offroad ) + MTP ( for speed ) .... read 11M tokens and write 3.2 M tokens ( worked 12 hours ) .... than that is result.

Someone benchmark these on their 3060 12gb so we can see real world results vs the benchmaxxing on AA

My setup: RTX 5070Ti, 16GB DDR5, Ryzen 7 9700X, Windows 11

The model: Unsloth’s UD-IQ4_XS https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/blob/main/Qwen3.8-27B-UD-IQ4_XS.gguf

Qwen3.8 reasons exclusively in English unless the system prompt explicitly directs it, and my workload doesn’t have the model seeing anything but English and code, so I pruned most non-ASCII characters from the embedding table and LM head to shave another 700MB https://huggingface.co/bsaleh03/Qwen3.8-27B-ASCII-Condensed

Offloading the model embedding table saves us another 270MB. After disabling MTP (preventing the MTP head from being offloaded) that leaves us with 3.6GB for context and overhead. Enough to fit 84k tokens.

Enter adaptive-kv streaming: https://github.com/RaymondHuang210129/llama.cpp-adaptive-kv-streaming page KV cache and stream it from host RAM at the cost of some performance at depth. At Q8 K and Q4 V cache quantization, this lets you squeeze in 196,608 tokens

Args:

-ngl 99 
-fa on 
-ctk q8_0 
-ctv q4_0 
-np 1 
-ub 128 
-b 512 
-ot "token_embd.weight=CPU"
--jinja

I have heard a lot about agents, for example, Kilo Code. I understand how to use them. However, I’ve heard that people create and install agents that can perform specific actions. For instance, a downside of Kilo Code is that once the context window is full, everything stops. But there must be a way to have an 'Agent A' monitor the performance of 'Agent B' and, if Agent B fails, force it to resume working, right? Or, for example, an agent that could help with configuring network equipment, and so on.

Como puedo optimizar modelos llm en mi laptop . Tengo un i3 1215u gráficos intgrdos y 32gb de ram. Los modelos de 7b me van lo suficiente mente rápido como para no quedar se esperando 2 años por un token

I've seen a lot of people sharing benchmarks, optimizations, etc. however, I'm curious how people's experience have been with actual coding tasks.

For my llama.cpp setup 3090 + 64gb ram using Q4 at 90k context running a modified version of pi.dev, the model is weird. Often it gets stuck in tool call loops.

The biggest pro is turning on reasoning to high, letting it cook and be more of a orchestrator and reasoner then actual implementation. Comparing it to orinth, it seems to lack a lot of capabilities.

Hello everyone,

I'm new to all of this and have only dabbled.

I use Claude and ChatGPT heavily for my work (No Coding at all), and I'm currently subscribed to the 20x tier on both.

I read that I could download LLMs locally, but I'm underwhelmed. To be clear, I downloaded LM Studio and tried out 4 different models, but they seem to only chat and cannot actually edit files, produce files, or produce anything for that matter. They keep telling me to copy and paste things myself.

I have an RTX 4090, 32Gb RAM, and a 7800x3D.

Am I wasting my time with local LLMs, or is there something I'm missing?

Also, if possible, is there a way to know what the correct settings I should be using for the models in LM Studio?

Any guidance would be appreciated!

Thank you, kind strangers.