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I'm testing it since release, now with UD 3.0 in my AMD R9700 with ROCm, I always read everywhere that F16 and q8_0 for KV cache are essentially the same... well, I tested it and I can see differences. Some differences are minimal, F16 is more careful and detailed with structured and free output, th
Like many of you I've spent the last few days throwing Qwen3.8-27B against all of my usual use-cases and personal tasks/harnesses and workflows. It's great, phenomenal sometimes, but that's not what this post is about. One of my little personal benchmarks is a little set of pocket trivia that's rele
Off a single prompt, given my credentials and the name of my university, qwen3.8-27b was able to successfully pull my class schedule from the kinda shitty and convoluted web of university websites. It needed no human intervention, and executed 80 tool calls. Another time, I asked it to investi
I've been working on a depth pruning approach and decided to try it out on the new Qwen3.8-27B model. I managed to get the model down to about 22.7B params without severe reasoning degradation. No fine-tuning was done, just strategic removal of layers. It's been working well for my use cases i
Howdy, I'm back again - running my favorite benchmark (it's still unsaturated for the time being so might as well!) previous runs a b https://github.com/michaelasper/benchmarks/blob/main/qwen3.8-27b-pi-on-slop-code-bench.md I ran this via OpenRouter because my mac would cry running 9 problems It di
(my own experience) I have a mid-range gaming laptop from 2024 with 4GB VRAM and 16GB RAM. There is nothing that I can run other than small models at humble t/s. I mostly use the models to generate scripts for medical physics research (don't worry, I verify them before any use, no one is getting va
llama.cpp pr #27342 adds dflash2, so i rented an rtx 6000 and ran the same four prompts through four decoding setups on qwen3.8 27B median results over the four tasks: baseline 47.4 tok/s mtp 114.7 tok/s dflash 99.3 tok/s dflash2 140.6. tok/s so on average 3x for dflash2 though i have to poi
Hello everyone, tried so hard to optimize my config and finally I simply get up to 70 t/s with q6 variant. And wanted to share with you guys so that other people with the same setup can enjoy. Please check out and see if that improves your performance in any kind of way. Also huge thanks to qwen and
Four Tesla V100s from 2017 matched my RTX 5090 on single-request Qwen 3.8 decode. Repo: https://github.com/dnv2003/v100-skinny https://i.redd.it/5ws2ak3uqckh1.gif The 5090 was not being held back. It ran NInfer, a specialist engine built to make this exact model as fast as possible on that GPU
Aloha! 🌺Introducing Ornith-1.5, a family of open-source LLMs spanning 9B Dense, 35B MoE, and 397B MoE, trained with self-improving strategies. It achieves state-of-the-art performance among open-source models of comparable size and delivers performance comparable to Claude Opus 4.8 across reas
Any LLM will hallucinate the world knowledge, even a 3T model. Use a 4B with a kiwix skill and local Wikipedia, 50gb and no more hallucinated world knowledge. Ask your coding agent to build your own, with your rules and eventual fallback access to internet knowledge for what's updated realtime like
PR by u/Stainless-Bacon 👍 It would be handy & awesome to have options --n-cpu-ffn / --cpu-ffn for Dense models like how we have --n-cpu-moe / --cpu-moe for MOE models. Also check his threads: On PR : llama.cpp CPU offload optimizations On Running Qwen3.8-27B with above optimization : Guide f
Intermediate tokens, called "thinking" or "reasoning" actually are nothing like it. Humans do step-by-step reasoning leading to the conclusion. LLMs use intermediate traces to augment their prompt. This explains why sometimes the answer is very good but the "reasoning" is verbose. Flooding you
I have been using local models on/off for like 2 years or so but never really used them extensively because the closed ones were always much better. Once Qwen 3.8 27B was released I decided to give it another serious try. I configured Cline and ZooCode as VSCode addons, installed a few MCP servers a
I hacked this together so there's probably more on the table in terms of performance. Measured with the Club-3090 canonical bench suite (bench.sh, 3 warmups + 5 measured runs, temp 0.6 / top_p 0.95 / top_k 20). Prefill: 1342 tok/s @ 10k, 628 tok/s @ 90k Spec-decode: 7 draft tokens, acceptance
I have been searching for suitable model to run on my 8GB RAM toy, NVIDIA Orin Nano Super 8GB. This little toy was priced at $249 earlier this year (not any more), and pulls very little power when idle. It was an interesting device that suitable for an agent to host on. It is likely won't do any cod
Apparently a second version of DFlash from the original authors of DFlash GGUF quants are already made available with an accompanying llama.cpp PR: https://github.com/ggml-org/llama.cpp/pull/27342 submitted by /u/rerri [link] [comments]
Who needs GPUs? submitted by /u/DeltaSqueezer [link] [comments]
Qwen released the 2.4T Max weights and I was curious how well it can re-create COD in one prompt I ran the model on a rented B200 cluster and used roughly 1.1M output tokens over a 5 hour time span Realistically barely anyone can run this model locally, but the fact that it's open weights unlo
I managed to run the 143–144 GiB DeepSeek-V4-Flash-0731 UD-Q4_K_XL GGUF on four RTX 3060 12GB cards while keeping a 360k–376k context window. Hardware: CPU: Intel Core i9-10920X, 12C/24T RAM: 128 GB DDR4-3200, quad-channel GPU: 4× NVIDIA RTX 3060 12GB Total VRAM: 48 GB Storage: NVMe SSD Engine
I was checking out benchmarks of this model and apparantly better than Qwen3.5 9b reasoning across the bench on artificial analysis. I have used the 9b model for variety of stuff and it has been amazing, but if this is better then why not switch. I am downloading it rn to test it irl, but ppl are we
Hi everyone, I use AI agents to code and manage my personal files heavily. However, I am worried that the agents may accidentally delete some important files on my machine, outside of the defined scope. I am wondering what sandboxes people are using. I am looking for a solution that is easy to use,
This is invisible with llama.cpp or derivatives, but ninfer helpfully logs the sampler settings on each request and auto-configures the correct ones for the model. Basically, OpenCode will always send top-p=1.0 (which means there is no filtering of low-probability tokens except with top-k) instead o
Results up front: I had to prioritize prefill or token generation - there was no happy medium. Using UD-Q4_K_XL, q8 kv cache, and 96k max context: focus on generation (MTP = 2): 350 pp and 36 tg @ 12k context focus on prefill (disabled MTP): 560 pp and 19 tg @ 12k context Focus on quality: using
I've been experimenting with Qwen3.8-27B using DeepSeek Harness. It's a monster at long-horizon tasks, and the results were pretty wild. DeepSeek Harness ran on my Windows PC and connected over LAN to NInfer on a separate RTX PRO 6000 box. The model was Qwen3.8-27B with a 262K context window. All sh
Overview UI-Mate-27B is an open-weight foundation GUI agent for long-horizon work across applications and operating systems. It observes live screenshots, reasons over the visible state, and produces structured keyboard and mouse actions for native desktop interaction. UI-Mate supports two com
I know why Qwen3.8 27B shipped with xhigh reasoning as default, it's to do its best in benchmarks. Models from top labs often get benchmarked at multiple reasoning levels, but that same treatment doesn't apply to other labs. Open models are lucky to even be benchmarked at all. (See Laguna S 2.1) So
This took one prompt to build, and another follow up prompt to fix two issues (player got stuck with the bomb and broken enemies path-finding), this is only html, css and js, no external assets, all done by Qwen. Using UD-Q4_K_XL in llama.cpp with 128k context and k5_0/v4_1 quantized cache in
submitted by /u/puffyarizona [link] [comments]
qwen3.8-27b looks genuinely impressive on the benchmark tables - beating models many times its size on some of them. but those numbers come from bf16 weights, and nobody here is running a 27b at bf16. we're running the 4-bit at ~17gb because that's what fits on a 4090 or a 24gb mac. so the model tha