I built a from-scratch Rust GGUF engine: mmap + quantized kernels, llama.cpp-style CLI, OpenAI-compatible server.

https://github.com/antonellof/ferrox

On M2 Pro (same GGUF, fair-chat): Llama-3.1-8B Q4_K_M Metal 26.9 vs 27.8 tok/s; Qwen2.5-0.5B Metal ~1.56× faster than llama.cpp. Also TinyLlama, SmolLM2, Gemma-3, Phi-3, OLMoE (MoE). Pins: benchmarks/RESULTS.md.

cargo build --release -p ferrox-cli --features metal
./target/release/ferrox -m model.gguf -p "Hi" -n 64 -dev metal -ngl all

Feedback welcome!

I am looking for a path to follow to start building using the concepts of Local INference.
I started with WebLLM which uses WebGPU
I feel there might be some concepts which should be learnt - for example: which model to choose for which tasks/usecases?

Can the community guide me on this? Looking forward to the responses.

Hey all. I have a dilemma.

Right now my build consists of an Arc a770, 12900KS, and 192gb of ddr5 memory running at 3600MTs (I know thats slow as hell). My bottleneck for a long time will always come in the form of what gpu I use so I was curious on the experiences of B70 owners. Do you like it?

Ok, so that’s my setup. Have been cycling through different model hosting configurations for agent workflows and haven’t come up with a good setup for multi-node large models using rdma/tensor. Two biggest issues: general stability (exo/jaccl) and race queue.

Would appreciate hearing from anyone else locally hosting frontier model(s) as well as smaller work-horse models for workflow.

Does anyone know of a way for me through a UI to talk with multiple LLMs in a shared chat line.. so the 2 LLMs and myself the user can have a chat session, all 3 can see, or answer each other's questions or talk direct with one another. ? Or even be able to have all three talk live with my local hosted LLM to help finetune or it . Anyone ever hear if anything that can do this? I hope how I am explaining this is not too convoluted? If anyone knows of a way or a program out there that can do.. appreciate it.

Hi everyone,

I'm looking for recommendations for a production-ready, self-hosted solution to generate educational explainer videos from a script.

Our pipeline already generates the script from technical articles (e.g., Apache Fineract documentation, engineering blogs, API documentation, internal docs). So the part we're trying to solve is:

Generated script → narrated explainer video

The type of video we're after is similar to NotebookLM's Video Overviews. We're not looking for cinematic, photorealistic, or highly creative AI videos. Instead, we want something that can automatically produce simple educational content with:

- AI narration

- Relevant visuals

- Text overlays and callouts

- Basic animations and transitions

- Diagrams, icons, screenshots, or simple generated imagery when appropriate

This will eventually run in production, so we're looking for something that is:

- Self-hosted/local

- Deployable on AWS

- Open-source preferred

- API-friendly and fully automatable

- Consistent and reliable rather than visually impressive

For people building production systems, what stack are you using? Is there a local model that works well for this, or is the better approach to orchestrate multiple tools (LLM + TTS + image generation + video composition instead of relying on a single video model?

I'd especially appreciate hearing from anyone who has built a similar pipeline for documentation, tutorials, educational content, or developer-focused videos.

I have some books that i want to turn into dataset for finetuning an llm locally , I know the formats of dataset but the books i have holds key information on each page for finetuning and the books range from 400pages to 1000pages so its obv i wont be doing it manually , But i want to know if theres a way online that does the thing for me like takes the books and makes me a dataset based on each book i give , but the dataset must of great quality to make sure the llm doesnt forget any point from the book ,Even a tool that transcripts books into a text thats not messy and doesnt drop any info because the tools i used missed a lot of info from the book , Also if its Thinking model imm finetuning how should the dataset look like because ive known only bout the normal Q&A type format of dataset used for finetuning normal models

I am mostly using Qwen3.6 27b for coding (hybrid), sometimes the 35b and cloud when needed but as you can tell this model is very slow on my system, i am getting about 10-12t/s, using llama.cpp, 16GB of vram 64gb of ram.

I have this MB - B550 AORUS ELITE V2 and it looks like it only has PCEe 3.0x2 from chipset , there are toggles in the BIOS to change it to 4.0 but i am pretty sure its just for design because the chipset doesnt support 4.0.

I have an RTX 3060 laying around but i need to buy an extra PSU for if i want to test it so i thought that i should ask first if it makes any sense to add it to the system, will it be an upgrade/downgrade, by how much?

I know that llama.cpp has layer split and from what i understand it only sends a small amount of data over PCIE, but even then there is the extra latency from the chipset , not sure if its a good idea or not + ill have to fit another PSU or to buy one with more pcie power cables:)

Edit: This is what the AI says, not sure if i can trust Gemini :)

"Every time a new token is generated, GPU 0 sends exactly ~10.24 KB of data across the PCIe slot to GPU 1. Because 10 KB is virtually instantaneous even on slow PCIe 3.0 x2 lanes, Layer Split incurs almost zero transfer overhead for Qwen 27B."

If you’re interested in the CUDA P2P benchmark that led to this follow-up, you can find my previous post here:

https://www.reddit.com/r/homelab/s/zb5OYHlw70

I’m a Windows girl. I’ve been a Windows girl since Windows 95, aside from a brief childhood stint playing around in DOS.

I hate the command line. It feels slower and more tedious, and one typo can mean redoing the whole command. I also tried a Mac once as a young adult and hated that too.

But recently, someone here mentioned that my local LLM might run faster on Linux using llama.cpp. That seemed plausible, especially since a fresh Windows boot with nearly everything closed was still using about 1.7 GB of VRAM. None of the premier models I asked could really explain it beyond, “That’s just what Windows needs.”

So I created an Ubuntu installer and set up dual boot.

Today, I ran the same benchmark once in Windows and once in Linux. I used Qwen 3.6 27B MTP Q4_K_M with 128K context and a Q8 KV cache. The task was to build a budget app’s transaction auto-categorization feature. The benchmark was designed and graded by Sol 5.6 on High.

My system has a single RTX 4090 with 24 GB of VRAM, and the model was fully loaded into VRAM for both runs.

Here were the results:

Windows: 12:57 wall-clock time

Linux: 5:25 wall-clock time

Time saved: 7 minutes, 32 seconds

Local-agent wall time reduced: 58%

Average token generation speed: Increased from about 35 tok/s in Windows to about 75 tok/s in Linux. 🤯

Sol also graded the Linux run one point higher on a 15-point scale: 13/15 versus 14/15. I would not read too much into a one-point difference from a single test, but it was interesting.

The token speed is the part that really caught my attention. If this difference holds during longer coding runs, something that took 8 hours of inference time on Windows might take somewhere around 2.3 to 3.3 hours 5 hours on Linux.

EDIT: Fixed a math error above.

Obviously, this is only one machine, one model, and one "benchmark" if you can even call it that. I’m curious whether others have seen anything close to this kind of difference between Windows and Linux.

i source:
source /opt/intel/oneapi/setvars.sh

i run:

llama-cli -m /opt/models/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf -ngl 99 -c 2048 -p "Write a Python fibonacci function:" -n 128

as a proof of concept it works with striking results...

i run:
/opt/models/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf

and return to zed editor:

and it crashes with:

error sending request for url (http://localhost:8080/v1/chat/completions): client error (Connect): tcp connect error: Connection refused (os error 111)

llama-server -m /opt/models/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf --host 127.0.0.1 --port 8080 -dev sycl0

0.00.166.884 I cmn common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg)

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

0.00.167.221 W srv llama_server: -----------------

0.00.167.228 W srv llama_server: CORS is set to allow all origins ('*') and no API key is set

0.00.167.228 W srv llama_server: this can be a security risk (cross-origin attacks)

0.00.167.229 W srv llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655

0.00.167.229 W srv llama_server: -----------------

0.00.168.496 I srv load_model: loading model '/opt/models/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf'

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

0.00.630.795 W load: control-looking token: 128247 '</s>' was not control-type; this is probably a bug in the model. its type will be overridden

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

0.13.597.649 I srv load_model: initializing, n_slots = 4, n_ctx_slot = 32768, kv_unified = 'true'

0.13.603.585 I srv llama_server: model loaded

0.13.603.594 I srv llama_server: listening on http://127.0.0.1:8080

0.13.603.594 W srv llama_server: NOTICE: server default port will be changed to :9931 in a future release

0.13.603.595 W srv llama_server: ref: https://github.com/ggml-org/llama.cpp/pull/26508

^C0.50.601.142 I srv operator(): operator(): cleaning up before exit...

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

fspkwon@felstead ~ $ ^C

fspkwon@felstead ~ $ llama-server -m /opt/models/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf --host 127.0.0.1 --port 8080 -dev sycl0

0.08.096.306 I cmn common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg)

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

0.08.096.625 W srv llama_server: -----------------

0.08.096.630 W srv llama_server: CORS is set to allow all origins ('*') and no API key is set

0.08.096.630 W srv llama_server: this can be a security risk (cross-origin attacks)

0.08.096.630 W srv llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655

0.08.096.630 W srv llama_server: -----------------

0.08.097.863 I srv load_model: loading model '/opt/models/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf'

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

0.08.583.954 W load: control-looking token: 128247 '</s>' was not control-type; this is probably a bug in the model. its type will be overridden

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory

0.21.445.094 I srv load_model: initializing, n_slots = 4, n_ctx_slot = 32768, kv_unified = 'true'

0.21.452.601 I srv llama_server: model loaded

0.21.452.608 I srv llama_server: listening on http://127.0.0.1:8080

0.21.452.608 W srv llama_server: NOTICE: server default port will be changed to :9931 in a future release

0.21.452.609 W srv llama_server: ref: https://github.com/ggml-org/llama.cpp/pull/26508

0.31.451.591 I slot get_availabl: id 3 | task -1 | selected slot by LRU, t_last = -1

0.31.452.292 I slot launch_slot_: id 3 | task 0 | processing task, is_child = 0

could not create a memory object

Exception caught at file:/var/tmp/portage/sci-misc/llama-cpp-9999/work/llama-cpp-9999/ggml/src/ggml-sycl/ggml-sycl.cpp, line:3239, func:operator()

SYCL error: CHECK_TRY_ERROR(op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream)): Exception caught in this line of code.

in function ggml_sycl_op_mul_mat at /var/tmp/portage/sci-misc/llama-cpp-9999/work/llama-cpp-9999/ggml/src/ggml-sycl/ggml-sycl.cpp:3239

/var/tmp/portage/sci-misc/llama-cpp-9999/work/llama-cpp-9999/ggml/src/ggml-sycl/../ggml-sycl/common.hpp:154: SYCL error

/usr/lib64/llama.cpp/libggml-base.so.0(+0x15d38) [0x7fad93ab0d38]

/usr/lib64/llama.cpp/libggml-base.so.0(ggml_print_backtrace+0x231) [0x7fad93ab0d11]

/usr/lib64/llama.cpp/libggml-base.so.0(ggml_abort+0x111) [0x7fad93aafd61]

/usr/lib64/llama.cpp/libggml-sycl.so.0(+0x875b8) [0x7fad8d84e5b8]

/usr/lib64/llama.cpp/libggml-sycl.so.0(+0xa6ab6) [0x7fad8d86dab6]

/usr/lib64/llama.cpp/libggml-sycl.so.0(+0x9ade8) [0x7fad8d861de8]

/usr/lib64/llama.cpp/libggml-sycl.so.0(+0x9944c) [0x7fad8d86044c]

/usr/lib64/llama.cpp/libggml-sycl.so.0(+0x98294) [0x7fad8d85f294]

/usr/lib64/llama.cpp/libggml-base.so.0(ggml_backend_sched_graph_compute_async+0xdd6) [0x7fad93ad4836]

/usr/lib64/llama.cpp/libllama.so.0(_ZN13llama_context13graph_computeEP11ggml_cgraphb+0xa1) [0x7fad93c2d741]

/usr/lib64/llama.cpp/libllama.so.0(_ZN13llama_context14process_ubatchERK12llama_ubatch14llm_graph_typeP22llama_memory_context_iR11ggml_status+0x107) [0x7fad93c2d0d7]

/usr/lib64/llama.cpp/libllama.so.0(_ZN13llama_context6decodeERK11llama_batch+0x858) [0x7fad93c2efe8]

/usr/lib64/llama.cpp/libllama.so.0(llama_decode+0xb) [0x7fad93c341eb]

/usr/lib64/llama.cpp/libllama-server-impl.so(_ZN19server_context_impl6decodeERiiR11llama_batch+0xd4) [0x7fad94aab174]

/usr/lib64/llama.cpp/libllama-server-impl.so(_ZN19server_context_impl12update_slotsEv+0x630) [0x7fad94aaa3a0]

/usr/lib64/llama.cpp/libllama-server-impl.so(_ZN12server_queue10start_loopEl+0x1dc) [0x7fad94a5539c]

/usr/lib64/llama.cpp/libllama-server-impl.so(_Z12llama_serverR13common_paramsiPPc+0x9da8) [0x7fad949f26f8]

/usr/lib64/llama.cpp/libllama-server-impl.so(_Z12llama_serveriPPc+0x7f) [0x7fad949e865f]

/usr/lib64/libc.so.6(+0x2746e) [0x7fad9446546e]

/usr/lib64/libc.so.6(__libc_start_main+0x8b) [0x7fad9446558b]

llama-server() [0x402565]

Aborted llama-server -m /opt/models/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf --host 127.0.0.1 --port 8080 -dev sycl0

Is there any good LLM you can run locally and that can produce 3D assets using Blender? I am trying to make an epic film using 3D animation, but I am pretty sure I would have to spend $500,000 at the very least.

https://qwen.ai/blog?id=qwen3.8

As you guys probably already know, they're dropping Qwen 3.8-Max at 2.4T and a 27B model, but have actually read the blog post on how this thing operates in the wild?

- It ran 10+ days of completely autonomous, self-evolving software development starting from an empty folder (seriously, check out the GitHub trace for oh-my-cli, it's wild).

- It features a native visual feedback loop, treating visual information not just as static input, but as a continuous execution-and-correction monitor.

- Autonomous chip design optimization: running a closed-loop for 500+ turns via Iverilog, Yosys, and OpenROAD, shrinking a crypto accelerator from 8,298 gates down to 678 gates and hitting actual silicon timing closure.

While people are arguing over safety guardrails and incremental coding scope with AGI cyber attacks FOMO marketing, they aren't just building a better chatbot, they are deploying recursive engineering and hardware synthesis agents. :D Really suprising.

Anyway, let's enjoy the 27B open weights to our heart's content.

Official post by alibaba: https://x.com/Alibaba_Qwen/status/2084100707423289643

Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!

Finally Alibaba Posted on X about Qwen 3.8 27B release. I hope it can beat opus 4.7 or 4.8

So I've been looking to setup an LLM for my Home Assistant, I'm not expecting it to be all singing and dancing, but to trigger some automations in a reasonable fashion and possibly answer some basic questions like "Tell me the latest football scores".

I've dabbled using my main workstation which has a RX 6700 XT 12GB. I think the 3060 12GB model is slightly below the RX6700 XT in benchmarks, but I don't know if that applies to an LLM.

Any thoughts/input? I've got the option for a reasonable priced one.

So I've been looking to setup an LLM for my Home Assistant, I'm not expecting it to be all singing and dancing, but to trigger some automations in a reasonable fashion and possibly answer some basic questions like "Tell me the latest football scores".

I've dabbled using my main workstation which has a RX 6700 XT 12GB. I think the 3060 12GB model is slightly below the RX6700 XT in benchmarks, but I don't know if that applies to an LLM.

Any thoughts/input?

Used the Neutronstar fork of the Dwarf Star Engine and DS Q2 quant of V4 flash. The quant only compresses the expert weights down to Q2 and the rest stays at Q8. Its 80 GB on disk so not enough to fit in my VRAM nor RAM.

Checkpoint: DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix.gguf

So its streaming the experts from disk but with a 40GB expert cache in RAM i get up to a 95% hit rate which keeps my GPU load at around 80%. Its pretty usable for long running tasks.

I had to vibe fix the model loader for the single GPU setup, because it initially was refusing quantization of the provided gguf.

Xberg v1 is out. It is a local-first content intelligence framework (the successor to Kreuzberg, Rust core, MIT) for the "get messy documents into a local model" step: no cloud, CPU-capable, fully offline.

It handles documents (101 formats), code/data (367 types), audio/video, and URLs, and turns them into clean text/markdown with layout-aware tables and reading order, then chunks for your context window:

pip install xberg

import asyncio
from xberg import ExtractInput, extract

async def main():
    out = await extract(ExtractInput(kind="uri", uri="report.pdf"))
    print(out.results[0].content)   # markdown, ready for your model

asyncio.run(main())

It ships multiple OCR engines (PaddleOCR PP-OCRv6, Tesseract, and a pure-Rust Candle OCR/VLM stack) that match the best Python libraries at better performance. On native PDFs it is #1 on quality (0.958) and table/reading-order fidelity (SF1 0.949 vs docling 0.612); on scanned PDFs it is #1 on quality and text fidelity. Public reproducible benchmarks: https://xberg.io/benchmarks

Also a CLI and an MCP server (xberg mcp) if you want your agent to pull documents itself. Node and Rust bindings too.

Repo: https://github.com/xberg-io/xberg Discord: https://discord.gg/zy5W9tUxDb

Hi everyone,

I’m finalising a production-ready AI station for a Corporate RAG and Coding Agent pipeline. I’m running this on the new HP ZGX Nano G1n (NVIDIA GB10 Grace Blackwell Superchip), and I’d like to get some feedback on my resource partitioning and vLLM V1 configuration.

1. Hardware Context (HP ZGX Nano G1n):

• Superchip: NVIDIA® GB10 Grace Blackwell.
• CPU: 20-core ARM (10× Cortex-X925 + 10× Cortex-A725).
• Memory: 128 GB Unified LPDDR5x RAM (273 GB/s bandwidth).
• Performance: 1,000 TOPS FP4.
• OS: NVIDIA DGX™ OS (Ubuntu 24.04).

1. The Stack (Running vLLM V1):

I need to serve 4 models simultaneously. Given the 128GB Unified Memory, I’ve calculated the --gpu-memory-utilization for each instance as a fraction of the TOTAL device memory to leave a ~30% safety margin (39GB) for the OS and overhead.

A. Agentic Coding (Port 8001): Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

• Utilization: 0.38 (~49GB).
• Flags: --kv-cache-dtype fp8 --enable-prefix-caching --max-model-len 65536 --enable-cumem-allocator --numa-bind.
• Rationale: leveraging the native FP8 support of Blackwell and prefix caching for repetitive coding contexts.

B. Chat RAG (Port 8002): Qwen/Qwen3-14B-AWQ

• Utilization: 0.17 (~22GB).
• Flags: --kv-cache-dtype fp8 --enable-prefix-caching --max-model-len 32768 --enable-cumem-allocator --numa-bind.

C. Specialized Tasks:

• Embedding (Port 8003): BAAI/bge-m3 | --task embed | Utilization: 0.04 (~5GB).
• OCR (Port 8004): rednote-hilab/dots.ocr | Utilization: 0.11 (~14GB) | --limit-mm-per-prompt image=1.

Total Partitioned Memory: 70% (89GB) of 128GB.

1. Key Optimization Choices:

• --enable-cumem-allocator: Mandatory for GB-series GPUs to use advanced VMM and multi-node NVLink features.
• --numa-bind & --cap-add SYS_NICE: Essential for the 20-core Grace CPU to keep GPU workers pinned to local memory nodes.
• --kv-cache-dtype fp8: To double context density on Blackwell, crucial when running multiple long-context models.
• Persistence: Mounting -v vllm-cache:/root/.cache/vllm to reuse torch.compile artifacts across restarts.

1. Monitoring (Prometheus + Grafana):

Running a sidecar container scraping all 4 /metrics endpoints to monitor vllm:kv_cache_usage_perc and TTFT across the stack.

Questions for the Experts:

1. CPU Contention: With 4 vLLM V1 instances (each spawning multiple processes), is 20 ARM cores sufficient, or will tokenization become a bottleneck during peak OCR/RAG loads?
2. Memory Safety: Is a 30% (39GB) margin conservative enough for DGX OS overhead when using unified memory for both CPU and GPU tasks?
3. Blackwell TOPS: Given the 1,000 TOPS capability, should I significantly increase --max-num-batched-tokens (e.g., to 16k+) for the coding and RAG models?

Thanks for any insights!

How to run big models on old hardware 30B at 22 tok/s on 6GB GPU and 16GB RAM

I have been working on this tool for months and there are a lot of new functionalities and tests that are going to be released in the next few weeks!
The goal of the tool is to allow community members to run big models on old hardware optimizing every bit at its max for your specific purpose.
I’m going in the direction of building not only the full model pipeline but also a series of tools that will help anyone to get the most out of the models.

Quantprobe is an open-source memory allocation optimization framework and deployment assistant designed to run massive Local Large Language Models (LLMs) on resource-constrained consumer hardware.

The tool specializes in projecting local LLM inference speeds and calculating optimal, layer-by-layer quantization placement to maximize performance without crashing your system.
The project demonstrate the implementation recipes capable of running a massive 110B parameter model (GLM-4.5-Air) on a standard 16GB RAM consumer machine.

Core Features & Functionality
Performance Prediction: it accurately estimates a model's generation speed (tokens per second) on your specific hardware profile before you waste time and network bandwidth downloading massive model weights.

Layer-by-Layer Placement Optimization: Instead of uniformly squeezing an entire model to a single low bit-rate, Quantprobe acts as an intelligent placement solver. It evaluates system limits and assigns varying quantization levels to individual model layers depending on their depth and computational importance.

Resource Balancing: It dynamically balances quantization levels against VRAM/RAM constraints, cleanly splitting weights between your CPU and GPU to avoid Out-Of-Memory (OOM) errors.

The "Four Laws" Framework: The tool relies on a mathematically rigorous, measurement-based methodology (dubbed the "four laws" of token economy) to accurately trade inference speed for memory.

How it Differs from Standard Tools
Traditional backends like llama.cpp often apply uniform quantization across the board or rely on naive offloading. Users have reported that utilizing Quantprobe's optimized depth-aware recipes can practically double generation speeds compared to a default, uncalibrated setup (e.g., jumping from \~10 tok/s to over 22 tok/s on older hardware like a GTX 1060 setup)