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specs hardware: M4 Max 128GB Studio inference engine: oMLX & lllama.cpp insights it still very early, so had to disable oMLX K/V caching, qwen4_exp architectureis not yet supported + the obvious n-grams with which the whole 4 bit quant takes ~100G, so pretty tight nevertheless, this is the fir
Since Qwen's dropped the Qwen4Exp architecture bomb that focus on offloading parameters to n-gram instead of pure mixture of experts, I dug into this and learned quite a lot. Here's the summary. Expect mistakes from human's writing lol. TLDR: MoEs do reasoning, N-grams do recalling. At the current t
https://www.businessinsider.com/nvidia-in-talks-to-buy-hugging-face-13-billion-dollars-2026-8 Edit: The Information is reporting that the deal is done: Nvidia Agrees to Buy Open Source Model Repository Hugging Face For $12.9 Billion (paywall): https://www.theinformation.com/articles/nvidia-agr
I ended up fine tuning a mistral 7b to outperform our costly foundational model and saved $300k. I previously thought that fine tuning was pointless (it's definitely not) and that all these problems could be solved with RAG (they can't). The truth is, a LoRA/QLoRA adapter is extremely useful f
*part 2 of an earlier post: previous quant comparison with voxel island creation this time I rented three rtx pro 6000 96gb, on each one I launched a qwen 3.8 27b quant and gave them 4 identical prompts: classical pool game air hockey 1v1 battle foosball official match demonstration bowling s
Hi everyone, it's been a while since I posted so here's an update on what the Lemonade community has been up to this summer. Our overall mission is to enable local AI builders with everything they need to make great apps and agents, while keeping the stack turnkey, portable, and modular. What
Upgraded to a 5070 Ti so I could run Qwen 3.8 27B, which works perfectly, but didn't want to let the old 4070 Ti go to waste. The cards would touch if I put them both in the PC and I knew the heat would be awful from my crypto mining days. I always was curious about eGPUs so I slapped together
The news about Qwen 3.8 Flash Next is the first I'm reading about n-gram tables. I may be completely misunderstanding how they work but it seems they could open the door for 1T+ parameter models to be run on a single server with modest GPUs and a ton of system RAM rather than needing a rack of GPU s
I was bored and handwrote a tiny 100-line bash script to let an agent search for and read articles from an offline wikipedia archive during a regular chat. It's not particularly useful, but it's definitely neat and a big step up from llama-cli. I got surprisingly good results even with LFM2.5-
Like wtaf? Qwen 3.8 27b is crazy. Can't wait for kimi k3 performance submitted by /u/GrokiniGPT [link] [comments]
Megathread for discussing the release of GLM-5.3-Flash. Quants Fine-Tunes & Abliterations Chat Templates Inference Server Support & Configuration Experiences, Benchmarks & Model Comparisons We'll try to clean up future duplicates around the release and point them here. Highlights GLM-5.3-Fla
Hi all, I'm looking for the best model for a hobby project and trying to make sense of the various data I came across. I know benchmarks do not often translate to the real world, especially to your particular use case (whatever it may be). But this is truly baffling: AA says Qwen 3.8 27B is better b
https://preview.redd.it/kbsqh6f7molh1.png?width=730&format=png&auto=webp&s=068dbea9a50be634a369d54d8b27b781d020fab3 My experience with Qwen 3.8 for agentic tasks has been phenomenal but I personally feel that 3.7 flash is more reliable for overall tasks. submitted by /u/Gohab2001 [lin
Megathread for discussing the (impending) release of Qwen 3.8 Flash Next. Quants Fine-Tunes & Abliterations Chat Templates Inference Server Support & Configuration Experiences, Benchmarks & Model Comparisons We'll try to clean up future duplicates around the release and point them here. Estimated
https://x.com/romanchernin/status/2092488160680751437?s=20 - Multimodal (Vision) - 1M Tokens Context Window - DeepSWE ~63% Edit: He deleted it, screenshot in comments submitted by /u/MrWidmoreHK [link] [comments]
Benchmarked qwen3.8 xhigh, medium and muse glimmer. Xhigh effort mode with qwen3.8 took almost 30hrs. (And still failed on 16 cases because of the 32K output token limit) Medium effort mode and muse glimmer were 3-4 hours each. But I'm actually surprised by the muse glimmer results, they came
So here's the thing, almost everyone use NVIDIA to run their LLMs, we also do the same, a lot of people we've met use like RTX PRO 6000 or even H100, B300 It seems like everyone eyes is looking at NVIDIA. However we do the math that the raw power alone on AMD GPU MI350X is higher than NVIDIA B200. S
With hopes of a Qwen3.8-35B-A3B release now mostly dashed, many people including myself are looking at fine-tunes and other variants of Qwen3.6-35B-A3B to run on VRAM-limited hardware. I decided to try to benchmark some of the top contenders: KAT-Coder, Ornith 1.5 and the very recent Tiel-Code
At $10k, you could get - 6.2B tokens with Qwen 3.8 Max (Qwen Pro plan) - 5.7B tokens with DeepSeek V4 Pro OpenRouter - 100B tokens with DeepSeek V4 Flash OpenRouter As a firm believer of local inference, unless you need it for data sovereignty, it's much more cost effect to wait for smaller models t
Granite-4.2-30B is the flagship reasoning model in the Granite 4.2 family. It delivers the strongest performance across reasoning-intensive tasks by leveraging built-in <think>...</think> chain-of-thought. It supports flexible thinking modes — full thinking (default), non-thinking, and low-eff
I ran 11 uncensored variants of Gemma 4 12B that I grabbed from huggingface, sorting by downloads. 10 full abliterations plus 2 LoRA adapters which were requested to be added in the comparison, against the official base. 165 GPU hours over three and a half weeks on a single 5090. Weight forensics, K
My best bet is the embedding-offloaded linear where the 51b n-grams track semantics and context, like 3.8 with its loss of real world knowledge bolted back on. Otherwise? Sparse full-attention with dense routing? 6b active route to 'heavy' layers when the n-gram gets stuck. Scenario 3: multi-head la
WeMM-Embedding-9B is a universal multimodal embedding model built on Qwen3.5. It accepts text, images, videos, visual documents, and interleaved multimodal inputs, and returns a 4,096-dimensional L2-normalized embedding. Audio input is not supported. https://huggingface.co/tencent/WeMM-Embedding-9B
Source of Claims: https://x.com/Andy_ShuoYang/status/2090856976880472439 Your gaming PC can now serve frontier models at interactive speed using official checkpoints without extreme quantization! Qwen3.6 35B → 8GB RTX 4060 laptop @ 39 tok/s DeepSeek-V4-Flash 284B → RTX 5090 desktop @ 22-25 to
It worked pretty well, digging through and analyzing some local files. Claude code regularly stops at some point and fails to continue while DSH worked for 2 h, recognized that it could benefit from reading more context and ... bummer: It left the project folder (although DSH was set up correctly) a
Hi, Back in late march I begun playing around with Qwen 3.6 27b and found like everyone else that it's notoriously good at tool calls, where every model I tried before just derailed after a few turns it kept going and felt quite reliable outside of typical behaviors of smaller modells. I decided to
Ornith does really well. TielCoder (https://llm-bench.io/benchmarks/cmt7kp2zj002r01lcmpchvlko) might be even a bit better in coding. Will give it a try soon. Details of the comparison see here: https://llm-bench.io/compare/runs?runs=cmt6ecf8g000001p45vwzux53%2Ccmt6ergk5000701p41hqdyy78%2Ccmt6f
TL;DR: I'm planning to spend around $100 on cloud GPUs to benchmark Qwen3.8-27B with a focus on questions that actually matter when running it locally: different quant levels/providers, 8-bit vs 16-bit KV cache, GGUF vs EXL3, context length tradeoffs, and token efficiency on coding/agentic workloads
Try it here: https://ocean.blackbeardlabs.dev/ Model: Qwen 3.8 27b Q8_X_KL Unsloth Hardware: 3 x RTX3090 Harness: DeepSeek Harness Prompt: /goal I want you to create a **JavaScript + Node.js WebGL project** that renders a highly realistic real-time ocean in the browser. Use **JavaScript on
submitted by /u/tarruda [link] [comments]