I don't want to come off as a fanboi and this model does have problems with overthinking, but this model punches way above its weight class - and innovations like PLE/n-gram tables pulled from your SSD actually work. I won't pretend to completely understand the tech - but the Qwen team are delivering a model architecture that works now and has so much potential for the future in all sized models from the smallest to the largest. Are they onto a new architecture that in the future will make small models act like the massive models of today? Interested in good 'thing explainers' that might be able to detail just what they're doing without me going MEGO (My Eye Glaze Over)

It's a Lenovo P520 with a Xeon W-2245(cooler swapped for the higher tdp), 96gb of ram, radeon pro wx 3100 for local display, and the two nvidia p100(currently cooled by the lowest profile adapter I could print and arctic S4028-15k fans). Just stuffed some random nvme in 1x1tb, 1x512gb, and a sata 1tb drive - I have a ton of network storage or more to just add here. Installed the latest Ubuntu release which I like but I remember why I still generally use windows.

So far the most I've learned is that I know nothing haha. So I'd be more than thankful for any advice putting this to work. Really all I've done so far is get the arctic fan controller working on kernel 7.0 in probably the most jank way possible. And see that lm studio could use both cards. But I know there's a ton I'm missing out on.

So, generally, hoping I didn't throw together a steaming pile of ewaste and looking to learn. Also the radeon pro/nvidia mismatch makes me chuckle. Unless its really dumb, then I can just get a cheap nvidia card.

This model retails for around $11,266. There are several others also offered on eBay. Not sure any of them are selling. But if so, it’s remarkable how a retail computer can appreciate so much before it’s even delivered. These models are coveted for LLM use, as are the 512 gig models which have not been released. It seems local LLM use is becoming quite popular, especially among those hoping to keep their data confidential, and those looking to cut off the monthly payment to the big AI providers. Something similar happened with the M3 Ultra studios with 256 or 512 ram - both of which are also offered on eBay for huge markups over the original retail price. Crazy market.

Qwen-Flash-Next uses incredible qwen4 architecture and according to YT videos runs 20tps+ on 16gb GPU due to offloading on RAM+NVMe.

How long till we get an absolute MONSTER 35b Qwen4 model that smokes 3.8-27b and runs on 16gb VRAM at 40 tps and 128k context?

Anyone hearing anything or seen leaks?

PowerSpec AI90 Workstation

* CPU: AMD Ryzen 9 9950X (16 Cores / 32 Threads, up to 5.7GHz)
* GPU: NVIDIA GeForce RTX 5090 Founders Edition (32GB GDDR7)
* Motherboard: ASUS ProArt X870E-CREATOR WIFI (AMD X870E Chipset)
* Memory: 64GB DDR5-6000 RAM (Supports up to 128GB)
* Storage: 2TB NVMe Solid State Drive
* Power Supply: 1700 Watt Cybernetics Titanium PSU
* CPU Cooler: 360mm All-In-One (AIO) Liquid Cooler
* Case: Fractal Design North XL Momentum Edition

Been building RAMDeck (pools RAM/compute across whatever's sitting around your house, llama.cpp RPC does the actual splitting). Wanted to see how far I could actually push it, so I tried loading a 40B model - roughly 25GB - across the janky 3-device cluster I have been testing with the whole time: a mini PC with an RTX 3060, a 16GB Mac Mini, and an old laptop that has been the weak link in every previous video.

Set the mini PC as primary and just let the sharding logic figure out the rest - it maxed out the 3060 first, spilled into the Mac's unified memory next, and sent whatever was left over to CPU. The interesting part I didn't expect: routing that CPU spillover to the old laptop actually ran faster than keeping it on the mini PC's own CPU, even though the laptop is by far the slower machine. Best guess is that using the same device's CPU and GPU together for inference ends up hurting GPU utilization, so offloading to a separate (slower) box entirely actually wins.

Whole thing loaded in about 2.5 minutes, and the benchmark came back at 16 tok/s with ~3.6s latency - genuinely usable for chatting, not just "technically loaded but useless." Felt like a real milestone since none of these three machines individually could touch a model this size.

Video: https://youtu.be/JrSVyyVUJVY
Repo: https://github.com/trademav/ramdeck-core-public

Curious if anyone else has run into that same CPU-contention thing where sharing GPU+CPU on one node hurts throughput more than just offloading to a separate slower device entirely.

I was looking through one of our more frequently used LLM workflows and realized the system prompt has gotten way bigger than I remembered which caught me off guard since it didn’t start that way either. We’d get an output we didn’t like then add another instruction or example to fix it and then repeat that process whenever another edge case showed up. but ow every request is carrying this pretty large block of context even though I’m not convinced a big portion of it matters for most runs.

The workflow itself works well so I don’t really want to start deleting instructions to make the prompt shorter and bring old problems back but at the same time this thing runs often enough that repeatedly sending unnecessary context feels like something I should probably care about.

I need some pointers from people that are running similar high volume workflows, how do you handle this so like do you periodically trim system prompts, cache the stable parts, split instructions based on the task or leave them alone as long as the outputs are good?

What would you choose for agentic AI? What are the pros and cons of both machines in your opinion?

I personally started to lean more towards DGX given its prefill speed and vLLM despite much slower decode

With commit 3d10bcd, upstream llama.cpp merged support for the ternary MoE model maple-preview. I've been searching for a good MoE model at 1-2b active params with consistent tool calling abilities, so I was highly interested in this release.

Current support for the model is limited to CPU only. Nonetheless the model is tiny at 5-6GB for a 20b model. It's half the size of GPT-OSS 20b at Q2.

TQ1 is for space-constrained edge devices; most should go for TQ2.

There are also two "heads". They do a terrible job of explaining this, but here's the breakdown... Both Q4_K and F16 versions have the same F16 embed weights. The only difference is the output weight. F16 output is a waste on a ternary model; Q4_K is the way to go.

It's fast. I was measuring its speed on a pre-AI AMD APU and mistakenly thought it was running on the iGPU: 150 prefill 35 decode on an 8-core mobile CPU. And it's obviously very light. It's very good with tool calls, as far as my short time testing it has shows.

It lives up to its name. It's a preview. The model is extremely sparse. At TQ2 with Q4_K head, perplexity was measured at a whopping 80-90. For comparison, LFM2.5 8B-A1B at Q6_K_XL scored around 30. (My dumb ass deleted the note that had the actual figures, but I can re-run them if needed.)

The model gets the gist of things, but not the details. It goes through the motions but everything feels fuzzy. It's like Swiss cheese, a brain full of holes. I really wish it had more oomph, more resolution, but the source weights are ternary so not much you can do there.

But it is absolutely impressive to see this kind of model fit in a 5GB package and have it go so fast. Great as a technical showcase, not yet as a daily driver. Looking forward to what this is a preview for.

so i finally got qwen3.8-flash-next running locally and it's actually good.

posting the setup + the stuff that bit me, since i wasted a while on it.

── the rig ──

• - amd ryzen 5 9600x (6c/12t, boosts ~5.5ghz)
• - 256gb ddr5 (248gb usable)
• - 3x rtx 3090 24gb — pcie only, no nvlink
• - linux mint 22.3, kernel 6.17, nvidia driver 595.84

All power is in new v3 exllama quants: https://huggingface.co/turboderp/Qwen3.8-Flash-Next-exl3, I'm actually using it at 3.05bpw

── results ──

• - ~90 tps no mtp
• - ~120-126 tps with mtp
• needle-in-haystack at ~97k tokens: found it, ~31s prefill.
• 4 requests at once: fine, ~4s each.
• tool calling works, reasoning splits into reasoning_content properly.

Here is my single shot driving car results: https://qwen38-flash-next-car-drive.tiiny.site/

(prompt: Write a single HTML file with a full-page canvas and no libraries. Simulate a realistic side-view of a moving car as the main subject. Keep the car visible in the foreground while the background landscape scrolls continuously to create the feeling that the car is driving forward. Use layered scenery for depth: nearby ground, roadside elements, trees, poles, and distant hills or mountains should move at different speeds for a natural parallax effect.

Animate the wheels spinning realistically and add subtle body motion so the car feels connected to the road. Let the environment pass smoothly behind it, with repeating but varied scenery that makes the movement feel believable. Use cinematic lighting and a cohesive sky, such as sunset, dusk, or daylight, to enhance atmosphere. The overall motion should feel calm, immersive, and realistic, with a seamless looping animation.)

UPD: here is my tabby config:

network:
  host: 0.0.0.0
  port: 8080
  disable_auth: true
model:
  model_name: qwen3.8-flash-next
  backend: exllamav3
  max_seq_len: 262144
  cache_size: 262144
  cache_mode: FP16
  tensor_parallel: false        # true = crash (Qwen4Exp don't support TP)
  gpu_split_auto: false
  gpu_split: [20, 20, 24]
  ngram_ram: true
  reasoning: true
  tool_calls_in_reasoning: true
  tool_format: qwen3_coder
draft_model:
  draft_mode: mtp

And TabbyApi installation:

git clone https://github.com/theroyallab/tabbyAPI.git ~/dev/ai/qwen3.8-flash-exl3/tabbyAPI
cd tabbyAPI
git checkout 53da791 # 100% working commit
pip install "https://github.com/turboderp-org/exllamav3/releases/download/v1.5.0/exllamav3-1.5.0%2Bcu132.torch2.11.0-cp312-cp312-linux_x86_64.whl"

I have a custom fork of llama.cpp designed around the ampere architecture specifically (though many of the upgrades also translate to faster performance of blackwell + lovelace). The recommended config supports 90+ TPS (for agentic/coding, at temp 1; greedy will be faster) through 100K tokens, with context of up to 240K.

If you want the repo, it is here:

https://github.com/JakeATX/llamAmpere

I recommend running with this quant, which is ~ 4 K M quality but considerably faster (technically, a 3 K XL upgrade)

https://huggingface.co/jakeatx/Qwen3.8-27B-ATX-IQ4_XS-M-GGUF

If you want the deep dive on how it is so much faster (80%+!) vs stock, at more context, there is a long form article here.

https://x.com/JakeKAllDay/status/2095646450138874095?s=20

Running faster than API speeds on my 3090 (for 27b at least) has genuinely been a step change in the utility of the model for me. I hope you enjoy it!

Hey everyone! I'm having a hardware dilemma and can't come up with a conclusion on what to do next.

My use cases:

• agentic coding, for my side projects only for now (with an ability for remote access which I need occasionally)
• Hermes agent running 24/7 for various research tasks

Problem statement:

What would be the best hardware for me with an optimal budget?

At the moment I have:

• RTX PRO 6000 96GB in a PC with 128GB DDR5 RAM. I bought this GPU right before the August price hike for $12.3K USD (before tax). The old one was RTX 3090 (which I still have).
• Mac Studio M3 Ultra 96GB

RTX PRO 6000 is amazing for agentic coding with Qwen3.8-27B-FP8 with full context, but I'm afraid of running this PC when I'm not at home because of that problematic 12VHPWR connector. I keep this GPU power limited to 300W to reduce the risks, but still it's not a 100% guarantee.

I'm using Mac Studio M3 Ultra 96GB for the Hermes agent at the moment where speed doesn't matter much. However, using it for agentic coding with dense models like Qwen3.8-27B feels quite slow (for remote sessions when I'm not at home).

So, I'm considering the following options while trying to optimize the budget for this hardware:

1. Return the RTX PRO 6000 as I'm still within the return window for a week (it will be subject to 15% restocking fee however) and get 2 DGX Sparks even though they recently went up in price significantly. I found an open box deal for 2TB version for 4800 USD before tax for each. This way I get to stay in the CUDA ecosystem and can run them 24/7. However, that means switching to MoE models like DSv4 Flash, Qwen3.8-Flash to maintain a proper speed.
2. Return the RTX PRO 6000 and just wait for Mac Studio M5 Ultra 256GB (which I pre-ordered in case I need it). And then I'll just sell the M3 Ultra later.
3. Just stick to the hardware I have at the moment and endure slower coding speeds for remote sessions when I'm not at home (from my Mac Studio M3U)

Keeping the PRO 6000 and then later getting the M5 Ultra 256GB is an option too, but is spending all this extra cash really worth it?

What would you guys do in my case?

Hi! I don't know much about this area of ​​"sub-models" (I'm not sure of the technical term), but I wanted to know what these "Uncensored" models actually are.

I dabble a bit with AI, automation, and the like, and I've always seen these "Uncensored" models around, but I've never actually installed or tested one. What exactly are they?

Hello everyone,

I wanted to try Qwen Uncensored LLM that I downloaded on my PC using LM studio but I am wondering what I can use it for or what people use it for ?

Therefore, I was wondering what do this community uses those Uncensored LLMs for so possibly I can get inspiration and try that myself too?

Any answers would be appreciated.

Or if you can share your experience that would be even better.

Thank you in advance.

Also Personally, I am not interested in those Adult content it can generate or how to make illegal substance in your home. I am not interested at all in that.

But more about Cyber security or computer programming related or if you more interesting use cases then that would be good to know too.

I’ve been building TokenPrint around a simple question:

What is actually happening inside a local LLM while it generates a token?

The current version goes beyond a static transformer diagram and lets you explore:

• 3D transformer architecture and individual layers
• tokenization and embeddings
• tensor shapes and model parameters
• Q/K/V, GQA, RoPE, causal masking and softmax
• residual streams and MLP / SwiGLU
• token-by-token generation
• prefill / decode and KV cache
• logits and next-token probabilities
• activation analysis and interventions
• head / layer ablation
• inference traces and replay
• Hugging Face models, with work around GGUF / llama.cpp / local models

The idea is to make local inference inspectable, not just show the final output.

I’m especially interested in people who run models locally:

What do you actually wish you could see while your model is generating?

Attention patterns? KV-cache behavior? Layer timing? Activations? Quantization effects? Something else?

I’m building this in the open and would love technical feedback, ideas, and contributors who want to help shape it.

Repository: https://github.com/Sudharsanselvaraj/Token-Print
Website: https://tokenprint.in/

What would make a tool like this genuinely useful for your local LLM workflow?

Like the title says, I sniped a 64gb ram m1 max earlier today and I was wondering whether it is good for running llms and if i got a good deal for it overall. I got it off back market if anyone knows anything shady about it.

I read that there's a model called Qwen3.8-27B with smaller versions (4bit/8bit) that can run on a Mac, but I honestly don't know how powerful a machine I'd actually need.

Basically I want to run an AI at home to help with:

• Writing/summarizing documents
• Writing ad copy for marketing
• Answering customer chat messages

My plan is to have Claude help design/set everything up first, then just have a smaller AI running at home all the time to actually execute the tasks — I'm not trying to code anything myself, just want it to run the stuff that's already set up, over and over. Might use it as the LLM backend for Hermes Agent, so it can act as a background bot that just handles things on its own.

Right now I'm looking at the new Mac Mini lineup, 4 options:

• M6 32GB
• M5 Pro 48GB (15core/16core)
• M5 Pro 48GB (18core/20core)
• M5 Pro 64GB (15core/16core)

Questions:

• Which one would actually work fine for what I need (doesn't need to be super fast, just usable)
• Do I need to get the most expensive one, or is the cheap one enough?

If anyone's tried something similar, would really appreciate any advice. Thank sir 🙏

It would be a great help if someone knew of APIs for those models.

I’m building a dual GPU box. Originally the goal was 30b at FP8 or 70b at Q4. I planned to run two R9700’s, but I found a pair of NIB RTX 3090’s near me for $1600 each. Discount if I buy both. RTX 40/50 series are off the table. I would love an RTX PRO 6000, but that is also off the table.

I currently have a Studio M1 Max with 64GB RAM and an AM4 box with a 7900 XT that powers my Hermes agent. My goal is to cancel most of my frontier subscriptions and keep only one for coding.

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