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Thanks to Unsloth Dynamic 3.0 quants coming in slightly leaner and better preserved, I settled on Qwen 3.8 27B (`UD_Q4_K_XL`) at 100K context as my daily driver for Hermes Agent and OpenCode. On an RTX 5070 Ti Mobile (12GB) paired with an Intel Core Ultra 9 275HX and 32GB DDR5, this configuration co
As per title. I'm not affiliated with the team behind this model in any way, shape or form. As a GPU poor myself (8 GB VRAM laptop + 12 GB VRAM desktop), I found Laguna to be very promising on my laptop. It runs at 30t/s (60k context) and it one-shot my two usual test prompts : "Make a Tetris-like g
It feels like a zig-zag: you don't want a model that's too dumb to do anything agentic. But once a model is good enough to be agentic, you don't want it to run so slow that iterating takes hours. For me the sweet spot is something like ~500 tps prefill, ~25tps decode. If a model isn't fast enough to
I ran the Aider benchmark on Qwen 3.8 27B FP8 with FP8 KV cache 256K context vLLM. The score: 72.9 This matches Gemini 2.5 Pro from 2025-04-12 which also scored 72.9. Beats Claude Opus 4 from 2025-05-25 which scored 72.0. DeepSeek R1 2025-06-06 scored 71.4. It may just be a random benchmark, an old
I just finished building my (relatively) low rent local inference machine: * Epyc 7663 * 256GB ECC DDR4-3200 * 1x RTX 5090 32GB Yeah I realize it's weird to throw a 5090 and 256GB of anything together and call it low end, but relative to ~151GB of weights it is. I'm running UD-Q8_K_XL and getting 23
Hi all, I made a post regarding how much Qwen 3.8 has improved over 3.6: https://www.reddit.com/r/LocalLLaMA/comments/1vqm51f/long_review_qwen_38_27b_is_very_good_at_tapping/ I made a very thorough write-up of how Qwen 3.8 compared not only to 3.6, but frontier models when it came to creating
Me and my team made Atomic Dynamic GGUF quants for Qwen 3.8 27B, so we wanted to see the difference between them by giving each quant the same voxel island creation task First of all we were surprised at how well Qwen 3.8 27B handled the 3D scenes in general, though part of that is probably be
I was just curious how the new qwen3.8:27b does on a hard C to HTML porting job against Opus 5 in a default Claude Code. The job: my fun side project is a procedural shooter in a single C file. Port it to a single-file html / three.js with one bot. One prompt, no follow-ups, no help from me. g
WARNING: I think I made a mistake with omlx, and its results below seem to be without any kind of acceleration. I am rerunning them with Lightning MTP and ANE support for prefill. Will update the post once done. The new Qwen 3.8 27B is fantastic for local agentic use. The problem is, what makes it
submitted by /u/MrMrsPotts [link] [comments]
Most YouTube benchmarks only show trivial tasks like generating landing pages or simple Three.js games. Is a local model like Qwen 3.8 27B actually capable of real-world systems programming—such as building GTK4 or Qt 6 applications in Rust or C++ with external libraries? Specifically, if I look up
Qwen 3.8 27B is great, however it takes me ages to do tasks on xhigh. I need Qwen 3.8 35B A3B. It'll be a little dumber but faster. I am also aware of the fact that 27B gets its "intelligence" from the long thinking time. I therefore assume that 35B would also be a long-thinking model, however runni
I have been lurking on reddit for like 6 years, and this is the first time posting, so apologies for the mistakes. I'm not a native English speaker, so sorry for the bad English too. I used LLM to translate / refine my post, including images. This is my attempt to compare the Qwen3.8-27B quant
What I ran: 8x B300 on Modal, $56.79 per hour, vLLM, tensor parallel 8, native MXFP4 Cold boot ~27 min (1.56 TB load, JIT, 51 CUDA graph captures) TTFT 0.92 to 1.02 s, decode 92 tok/s steady, 83 tok/s average over 4 prompts $190 per million output tokens. One clean run is about $36 of GPU tim
I don't know about you guys, but Deep-seek harness is insane. It's not focused on being a coder agent, it's webUI made it very easy to just checkin from time to time, and the best part? Why it's better than Hermes? It wasn't frustrating at all to setup. ZERO. NADA. Progressive setup is such an impro
I’ve tried Open Code , Codex and Qwen Code and so far Qwen Code seems to work the best for me , the only complaint I have about it is that the CLI interface is a bit rough / sloppy . submitted by /u/HelloWorld-Print [link] [comments]
Our devs got their hands on it a few days ago. One wired it into Codex to compare with GPT Luna, our usual workhorse right now for its cost effectiveness. Another tried it out on one of our OCR pipelines. It's comparable to Luna for coding and ***OCR quality appears to be better than Gemini 3.5 Fla
Companion to the Qwen 3.8 Release Megathread. Compiled from ~2,000 posts scanned across both subs, with deep reads of the 45 highest-signal threads (560 posts and comments), Aug 15–22, 2026, plus independent X benchmarks. Every number is attributed to the poster's stated hardware/runtime/quant. This
Gemma 12B is obviously a very well trained model, I always thought the fine tuning they did on it wasn't really cut out for agentic coding. From my own experiences it struggles to use the tools it's given from Github Copilot and is also very inept at the cli too. So I thought I'd kill two bird
I'm running Qwen 3.8 27B locally on a single GPU. I can push the context to 131k, but I'd rather run it faster at 64k if the agent can manage context properly. What I have in mind is pretty simple: one model stays loaded the whole time main agent gets 64k when something is too big, it spawns a fres
Further to my last post, https://www.reddit.com/r/LocalLLaMA/comments/1vldngi/tested_in_coding_bf16_muse_glimmer_vs_bf16_qwen36/, I bring this requested comparison of Qwen3.8 27B. Context: Both models are running at full FP16 KV-cache. Due to RAM limitations, BF16 Qwen3.6 was running at 150,000, whe
I read a lot of posts like "I've given this prompt to my Qwen 3.8 27b and it one-shotted me a 3d game after 24 hours". I wonder, what's the best harness for tasks like this? It has to have auto compaction and probably a memory system, right? And excellent computer use and self analysis. submitted
Hey guys, Inco AI shipped DFlash 2 a few days ago with a drafter for Qwen 3.8 27B and a llama.cpp PR. I built the PR and ran it against plain decoding, MTP, the n-gram lookup drafters, and my July DFlash 1 numbers on Qwen 3.6 27B for 3 days. One RTX PRO 6000, concurrency 1, about three days of
Im looking to set up a new local llm (probably on unsloth studio as that seemed to be doing pretty well last time I tested it). This one won't need to do agentic coding or app building or anything (not this time) but instead more 'text' based tasks such as - being given reference images and instruc
I was finally able to replicate tensor level allocation outside the Gemma family. https://huggingface.co/ByteOtter/Qwen3.5-4B-CADA-IQ2_XS After the Gemma 4 12b, e4b and gemma 3 4b results, I attempted to expand into qwen and ran into a few walls. After 2 version updates and a slightly differen
I was experimenting with deepseek harness when found that even if you don't use deepseek models, you can configure the web_search tool with their api key and every hit will cost you as if you called deepseek-v4-flash model. It's a bummer. on top of that there is currently no other free web search p
Idea is simple-ish in abstract: instead of running normal prefill over all of a given prompt, split it into parts - generates caches for part A and part B in isolation, concatenate the result, feed it into decode like normal. I honestly thought it'd totally fail. But I've been trying it out on Ling3
You can find the changelog and source code here: https://github.com/ggml-org/llama.cpp/releases/tag/v0.2.0 Associated pre-build is here: https://github.com/ggml-org/llama.cpp/releases/tag/b10566 submitted by /u/PhilippeEiffel [link] [comments]
Context: Earlier, open-source large models like Mimo V2.5 Pro, DeepSeek V4 Pro (first version), and Kimi K2.5 used to struggle with this prompt, and Qwen 3.6 27B couldn't even render the globe properly. But now, Qwen 3.8 27B is so much better than the previous model. I hope qwen publishes some
Sharp is u/peculiar-ragdoll's system prompt that makes Qwen answer way more tersely without losing correctness. It's built on top of froggeric's fixed chat templates for Qwen; several fixes now in the v22.x templates (error-escalation tiers, false retry-loop kills, multi-system merging, correct tool