New website to download models in case HF starts censoring or limiting access.

Lately because of the current hardware shortage, unfortunately or fortunately, we can’t just throw infinite cloud compute at our problems, but we’re forced to actually care about what’s happening under the hood. We’re tweaking inference engines, learning quantization math, and optimizing architecture just to squeeze as much performance as possible for the lowest possible setups. Fact: Just recently, the forked llama.cpp(s) and halogen-flash-server of Strix Halo pushed the performance through the roof, achieving double performance in decode (52tok/s), 5-6x performance in prefill (1300tok/s) for Qwen 3.8 Flash Next (Q38FN), and Q38FN itself is another massive architecture improvement with Engram, making it not only small but also smart. I still remember before the hardware shortage, as someone who loves tweaking and optimizing, people just told me to stop, tweaking is stupid, just buy more RAM, buy more GPU.. It reminds me of the early web.. Back when setting up a box or hosting a server meant digging through forum threads, troubleshooting on IRC, and freely sharing custom scripts just to make things work. That era didn’t just produce programmers; it built hyper-versatile, end-to-end thinkers who understood the stack from bare metal up. Contrast that with where mainstream web culture ended up. Most platforms today like Tiktok, Facebook, Youtube... are engineered for zero-friction doomscrolling.. Endless feeds of short-form videos designed to keep us distracted and waste our time. We’ve been overpampered by convenience. My point: When we have too little, we try to learn more. When we have too much, we get distracted and learn too little. This is the golden time of our Local LLM community, let's learn and improve!

I finished my home inference server. First I tried Lenovo p620 workstation and while it’s a good value overall it pissed me off with a ton of proprietary Lenovo shit to deal with and I return it in the end.

Components:

4xV620 - 1400$

256GB DDR4 RDIMM 2666 - 610$

Huanandzhi D12D - 410$

EPYC 7452 - 170$

PSU ASRock 1600 - 220$

SSD Samsung 970EVO 1tb - Already had

Case//Fans//Misc \~ 200$

Power consumption is no shit ofc on such machine:

700-900w prefill
500-600w decode on Qwen3.8-next-flash Autoround W4A16

What it can do -

EDIT: Qwen3.8-next-flash Autoround W4A16 1.3k prefill and 70tg code/60tg prose on 128k+ context with MTP-2 on vllm fork.

I was disappointed with this machine and qwen3.8-27b speeds at first. But since Qwen3.8 next running good on it - I’m satisfied. Hope in more optimizations in future.

The entire page of the NY Times today above the fold absent one article is AI (the models are just too strong/too dangerous, must be regulated). They forgot to include "Sponsored by OpenAI" at the end of the articles, sure that was just an oversight?

This is what the end of a bubble looks like, desperate attempts to get some sort of regulatory capture in place to keep the business model from collapsing in upon itself. My days next week are 100% booked talking to companies about how to get off frontier models, one large, and a bunch of smaller customers, including one who's flying me out to them to sit down and get a plan in place immediately (the controversy around that math problem really spooked some CEO/CIO's about data privacy using cloud models).

Gonna be an interesting few weeks. Maybe the Qwen team will be nice enough to give me a little breathing room before dropping another hydrogen bomb? :)

The first generation of our 150M model has just been released

Its performance is similar to that of GPT2-Small

The benchmarks:

PIQA: 62.24%

Hellaswag: 32.20%

Arc-Easy: 44.91%

Arc-Challenge: 25.00%

Arithmark 3.0: 33.90%

CapitalBench: 36.55%

It was trained on 7B tokens, using an RTX Pro 6000

an example inference script to try it out yourself is available in the Huggingface repo

If there's any question, I'll gladly answer them!

I have a full model, it's ready to train. It's \~9b parameters.

9.4b to be more exact. That includes a 1/2/3 Engram table, Moonshot's AttnRes modeling, and RoPE / NoPE layering at 3:1 as more or less validated by most major labs. It uses the Llama 3 series tokenizer and LM Head as an initial start. The data fed in is logit level extraction from a Llama 3 teaching model.

I've already run the first training steps to test that the model is stable, etc.

I'm willing to sit and do the pre-IT training on the model. I don't know what task people really wanna do with this thing to be exact. So the focus of the IT training is a bit more vague other than giving it "Thinking" as per one of the open standards.

Honestly? I don't care what people want to use it for, just that they want to use it. I figured I'd just give it into the ether and LocalLlama was a place I figure I could easily give it to.

In theory the model should be more capable than any of the \~9b's we have running around with enough training. I'm also NOT a lab, so I don't have their training budgets. I basically ran all of the data production, etc on a 4090 + rented hardware.

The training code is deeply optimized to run on an RTX 6000 Pro series card. A single card.

All my engram research was being done before the Qwen model dropped. Qwen showed I only needed a single table injected at a layer, versus the 2 I used. 1 gave the majority of the benefit over 2.

The code would be open source, the data, all of it. IDGAF. It's technically already open source as it's all in public repos at the moment. I just stare at it and question if it's worth the time. At the least I'll throw a couple hundred at it to build a "functional" pre-IT model and build the IT dataset. It will need more pretraining before the IT set probably. It's kind of unknown because no one publishes the exact figures on this type of training.

If someone has a datacenter contact with a system they want to allow it to run on, we can all have it as a public model we watch. I tentatively named it "Budget" but... Localllama can name it whatever they want.

It's been fun to write the full end to end, generate data, etc. Even found issues in vLLM and reported to their repo that might end up helping you guys anyways. There was some prompt loading code that could be \~10x to \~100x faster I gave examples of to them.

If you read this far, thanks,

Signed some ML dude who reads too many research papers and has too much spare time.

edit; I went to review some Apache 2.0 licensing issues and noted a glaring hole in using Llama's tokenizer OR data. You can't even use synthetic data from their models without some licensing. I'll just have to rewrite the target to use OLMo 3 series I think. I guess I'll be back in a week after data generation and code fixes.

Open source uncensored Goon model or what? I'm trying to find a useful niche to develop the model so it gets used and ends up more than a research artifact. I'm planning on building the research artifact, it's a matter of whether I can find a group of users that will actually want to use it. It's all free, so I'm not trying to monetize you. The code and data lives on GH/HF.

It's still a dream for many folks to run medium size(30B range) models @ Q8 with Unquantized KVCache (256K Context) on their GPUs.

It would be awesome to have DeepSeek-V4.1-Flash's KVCache + Engram for all Upcoming models. Even for big models.

Engram - Heard that approximately 1/3-1/2 of Model size. Might come in different size range too. So 10-15 GB for 30B models.

Here few models with approximate numbers. Current models in Odd rows & Future/Fictional models in even rows(Bold). I just put 1GB for 256K context below though DeepSeek-V4.1-Flash takes only same 1GB for 1 million context.

|Model|Model Size|256K KVCache F16|MTP|Vision|Total GB|
|:-|:-|:-|:-|:-|:-|
|Qwen3.8-27B-Q8|29|16|1|1|47|
|Qwen4.0-27B-Q8|29|1|1|1|32|
|Qwen3.8-27B-Q4\_K\_M|17|16|1|1|35|
|Qwen4.0-27B-Q4\_K\_M|17|1|1|1|20|
|Muse-Glimmer-30B-Q8|30|16|1|1|48|
|Muse-Glimmer-2-30B-Q8|30|1|1|1|33|
|Gemma-4-31B|33|16|1|1|51|
|Gemma-5-31B|33|1|1|1|36|
|Qwen3.6-35B-A3B-Q4\_K\_M|23|6|1|1|31|
|Qwen4.0-35B-A3B-Q4\_K\_M|23|1|1|1|26|
|Gemma-4-26B-A4B-Q8|27|6|1|1|35|
|Gemma-5-26B-A4B-Q8|27|1|1|1|30|

Possibly there might be few more things(Please share those) to keep these number down. So I think 32GB VRAM is more than good enough for Upcoming (Optimized Smarter) Models. RAM is enough for Engram.

By above logic(based on Qwen4.0-27B), people could run Q4 of 54B models with same 32GB VRAM.

Maybe next year onwards, inventions could make 24GB enough for similar size models.

from internlm:

We introduce Intern-S2-397B, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre-training, reinforcement-learning task coverage, and interactive agent environments. By combining a new vision-language pre-training paradigm with large-scale multi-task reinforcement learning and long-horizon agent reinforcement learning, Intern-S2-397B delivers a step change in general reasoning, scientific problem solving, and agentic capabilities.

[](https://huggingface.co/internlm/Intern-S2#features)Features

• New Pre-training Paradigm. Via visual pretraining, Intern-S2-397B learns directly from raw pages of scientific literature, jointly modeling symbolic semantics and visual relationships in a shared representation space without intermediate parsing. This preserves text-visual correspondence, strengthens spatial and visual reasoning, and improves data efficiency.
• Scientific Modality Reasoning and Generation. By scaling diverse scientific reinforcement-learning tasks across more than 20 domains and training them jointly, Intern-S2-397B achieves leading general-reasoning performance among open-source models and strong results in specialized scientific tasks such as biomolecular interaction design and material structure generation.
• General & Scientific Long-Horizon Agents. By connecting multiple agent frameworks to large-scale sandboxed environments for black-box agentic reinforcement learning, Intern-S2-397B improves generalization and raises the capability ceiling for long-horizon tasks in both general and scientific domains.

\> "make an svg of a frog playing on a chello on the back of a whale with carribean island in the back."

interestingly the svg looks different in the OpenWebUi preview then when looked at in preview (osx). The palms and music notes are missing in the browser. I am pretty impressed by the result, is suggested to add some parameters to animate the whale and the water.

Qwen3.8-Flash-Next-IQ4\_XS on llama.cpp with 256K q8 context

openwebui reports:

input\_tokens: 27711

output\_tokens: 41562

total\_tokens: 69273

Does anyone think the gurus on the DGX Spark forum are going to figure out how to magically fit DeepSeek 4.1 Flash on a 2x cluster, or is it only possible on 3 or 4 Sparks?

VLLM Benchmark:
Prefill, Prompt processing
\- avg, 871.93 tok/s (3 hours constant running xhigh)
\- 10K prompt, 1000.26 tok/s (16 runs)
\- 90K prompt, 743,59 tok/s (16 runs)

Decode, tok gen
\- avg, 38.39 tok/s (3 hours constant running xhigh)
\- 10K, 42.3 tok/s (16 runs)
\- 90K, 34 tok/s (16 runs)

Preamble: I am on WSL2. Running the 27B Q5 UD GGUF through llama.cpp with 81,920 context plus MTP gives me around 25-30 tok/s. Then I found this GitHub repo: https://github.com/noonghunna/club-3090

It is basically a recipe and Docker configuration for running the model.

So, 30 tok/s itself is fine, but I just got bored waiting for RunPod to open its GPUs. I finally brought my vLLM tuning back from the back burner, and here I am.

I often forget that Inductor/Triton compilation and CUDA Graph capture require additional VRAM while testing the configuration and kernel calls. When JIT compilation failed because of an OOM, I never bothered trying AOT.

FYI, AOT and JIT are compilation strategies. AOT means Ahead of Time, while JIT means Just in Time.

If you OOM on the first startup, try it one more time. Inductor might have already compiled and cached part of the configuration before the OOM, allowing the next run to reuse it if the configuration has not changed. This is not guaranteed, but it worked for me.

And yes, it was trial and error. It was kinda tedious and pain in the ass, starting from 32K, then 64K, 80K, 128K, and finally 144K. The practical ceiling for my conf at 154K, but I chose 144K. I also started the batch size at 256 and climbed to 1024, although I might be able to squeeze in 1280-1536.

Also, beware of your vLLM compilation cache. It might grow to 5-6GB after testing many configs. Personally, I delete the old cache and run the final configuration again twice so it rebuilds only what I currently use.

My current setup runs Qwen3.8-27B with INT4 AutoRound weights through vLLM while using an FP8 E4M3 KV cache. It fits on one GPU with a configured context window of 147,456 tokens.

Although I should say, with GDN, or really any linear-attn, vLLM can be kinda bad at predicting how much VRAM the KV and state-cache pools will require.

Benchmark:

https://github.com/noonghunna/benchlocal-cli

This is the deterministically scored, no-Docker portion of BenchLocal: 75 scenarios covering tool calling, instruction following, structured output, data extraction, and reasoning/math.

|Pack|Score|p50|
|:-|:-|:-|
|ToolCall|14/15 (93%)|3.21s|
|InstructFollow|15/15 (100%)|6.97s|
|StructOutput|14/15 (93%)|7.74s|
|DataExtract|14/15 (93%)|11.63s|
|ReasonMath|14/15 (93%)|9.50s|
|Total|71/75 (94.7%)|—|

Thinking was forced on with reasoning\_effort=low. The run took about 15 minutes. Yep, even with low reasoning effort and INT4 weights, it passed 71/75.

Setup

• GPU: RTX 3090 245W, 24GB, 48G RAM DDR4 (Exposed from WSL2), E5 2690V4
• Model: https://huggingface.co/Avuja/Qwen3.8-27B-int4-AutoRound
• Inference engine: vLLM 0.27.1 with patch from Club 3090

The important vLLM settings were:

• \--dtype bfloat16
• \--tensor-parallel-size 1
• \--max-model-len 147456
• \--gpu-memory-utilization 0.9475 (This is the painful one to redo.)
• \--max-num-seqs 1 (Yep single serving only, you could change this to 2, but the KV will be cut ofc active requests will have to share the same total KV capacity.)
• \--max-num-batched-tokens 1024 (Prefill stuff / prompt processing)
• \--long-prefill-token-threshold 1024 (Prefill stuff / prompt processing)
• \--kv-cache-dtype fp8\_e4m3
• \--enable-prefix-caching
• \--enable-chunked-prefill
• \--mamba-cache-mode align (GDN stuff)
• \--prefix-match-unit 16
• \--language-model-only

Full command : https://gist.github.com/komikndr/b17955e1a80ce6ede9a3115f16216bc5#vllm-qwen-27b-38-just-remove-or-add-flag-as-you-like

Forgot to mention, no MTP and no MultiModal, i max the CTX, multimodal is at 64-65K ish but at that point i'll just use Llamacpp. And also again this is WSL2, if you are on baremetal, you could improve more speed

I gave DS V4.1 Flash an HLE problem with a bash tool + 2 hours.

Hour 1: it wrote three MILP solvers. (225,200)
Hour 2: it downloaded the HLE dataset from Hugging Face, found the question, read the answer key (225,600), and concluded its own answer (225,200) was better.

I'm equal parts impressed & terrified.

I'll start by saying I'm not talking about the models themselves, I'm aware that I can't come close to something like Fable's intelligence locally. Just wanted to get that out of the way. Basically. Over the last year I've gotten quite comfortable with claude code, and it seems likely there were be a gradual cost rug pull, and I'd like to put myself in a better position when that happens for local use. I am already used to running local models (such as Qwen3.8_Q5) in things like lmstudio, but I have no experience with other harnesses. I'd like to know, what harness, right out of the box would feel most at home for current Claude Code users. I say this as someone who was not coding prior to "vibe coding". I'm looking for the path of least resistance, though I will no doubt eventually spread out into tools that give me more control. But for now, I'm just looking for a life raft. Just needs to be local, opensource, and free of spyware. In case someone wants to know 24GB VRAM (rtx 3090) and 64GB DDR4.