So apparently the model gets beaten by qwen 3.6 on every benchmark reported by cohere labs. You are getting lower RAM (considering model offload) usage and slightly better performance for imo significantly less output quality.
model
Qwen3.6-35B-A3B
huggingface.co/Qwen/Qwen3.6-35B-A3B ↗
5910215 downloads2016 likesimage-text-to-texttransformers
from the model card
Qwen3.6-35B-A3B [!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. Qwen3.6 Highlights This release delivers substantial upgrades, particularly in Agentic Coding: the model now handles frontend workflows and repository-level reasoning with greater fluency and precision. Thinking Preservation: we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead. For more details, please refer to our blog post Qwen3.6-35B-A3B. Model Overview Type: Causal Language Model with Vision Encoder Training Stage: Pre-training & Post-training Language Model Number of Parameters: 35B in total and 3B activated Hidden Dimension: 2048 Token Embedding: 248320 (Padded) Number of Layers: 40 Hidden Layout: 10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE)) Gated DeltaNet: Number of Linear Attention Heads: 32 for V and 16 for QK…
discussions
- Qwen 3.6 39 ongoing since 2026-06-05
recent items
DOA model by Cohere Labs (www.reddit.com via reddit) Qwen 3.6 35b A3B Speed Help (www.reddit.com via reddit) Jetson Orin NX Build for Hermes Agent + Benchmarking (www.reddit.com via reddit) I had a huge LLM server, and now I have a tiny one! I had a Jetson Orin NX gathering dust from a long dead robotics project, from back in the Llama-7B days.
Gemma 4 31B's competence surprised me (www.reddit.com via reddit) I'm just getting started using local LLMs for code. I'm not interested vibe coding, but I am hoping to increase my productivity in the publish or perish world of academia.
Qwen 3.6 for coding with 5090 - Your settings recommendations? (www.reddit.com via reddit) Hi, totally new to using LLMs for coding purposes, I am on Ubuntu and currently using LM Studio with Qwen 3.6 27B Q4 on a 5090. Finding it slow and context runs out fast.
Pursuit of performance Llama.cpp to MLX (www.reddit.com via reddit) Right now, I am running llama.cpp on a M2 ultra 64gig. Having great fun with unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q8_K_XL - Running opencode and finding it amazing to have such great tools running locally.
2X tk/s (from 19.4 -> 38.1 tk/s on 1 x MI50) Playing with a hypothesis like speculative decoding.. but instead of an additional side model, exploiting that I can run multiple computations side-by-side AS IF I had Qwen3.6-27B loaded twice in memory - small quants don't use all the available compute. (github.com via reddit) Forgive the claude summary, in the readme, but the base works. I'm still working on the hip kernal and having it combine with MTP.
[Follow-up] Qwen3.6-35B-A3B 8GB RTX: I tried Linux, tested Gemma 4, and now understand why Windows was faster (www.reddit.com via reddit) Original post: https://www.reddit.com/r/LocalLLaMA/comments/1txwff3/comment/oq1e0jt/?context=3 TL;DR: Migrated to WSL2 to test Linux (several people suggested it). Embedded MTP on the UD model: 25.8 tok/s.
Me: Arguing with an AI bot who just posted something on this sub about Llama 3.1. (www.reddit.comhttps) For real tho, these bots need to turn on their web search functions and quit living in the past. It’s bad enough we gotta deal with all the “Qwen3.6 27b helped me quit drinking and brought my dog back from the dead” posts.
Qwen3.6-35B-A3B tool calling benchmark: ByteShape vs. Unsloth GGUFs, KV cache quants & long context performance (www.reddit.com via reddit) I've previously posted some small performance benchmarks, but this time I got interested in the qualitative side. u/Substantial_Step_351 posted a few days ago about why models are not benchmarked on tool calling, and u/complexminded pointe…
5070 Ti + 5060 Ti on vLLM hangs on GDN with Qwen3.6 (www.reddit.com via reddit) [3090] Gemma4 QAT + MTP quick TPS numbers [TLDR 1.2-1.8x better] (www.reddit.com via reddit) These last few weeks have been godsend for 24GB (and below) gpu poor peeps. Killer models released (Gemma 4 / Qwen 3.6) Free intelligence via QAT Bonus speed via MTP We're at the tipping point where GPU poor (24gb and below) people are act…
[Benchmark] DFlash Speculative Decoding + KV Cache Compression on RTX 5090 — 3.26x Speedup (www.reddit.com via reddit) Hardware: RTX 5090 | Model: Qwen3.6-27B | Framework: BeeLlama.cpp Full benchmark scripts, raw data, config, and generated artifacts are available on request — just DM or comment below. I spent the last week benchmarking DFlash speculative…
Weird to get near linear scaling by adding another GPU? (www.reddit.com via reddit) Single steam benchmarks (club-3090) model: qwen3.6-27b-autoround-int4 BEFORE: 1x3090 *Their default script recipe for single 3090'*s (4-bit quant and 4-bit kv cache, mtp=2) NARRATIVE decode_TPS: mean = 53 std = 0.6 CODE decode_TPS: mean =…
Show HN: Best setup local LLM found for a 5090 (llama.cpp fork + turboquant) (local-llm.utop.workers.dev via hn) Hi folks, I found this setup on consummer hardware that seems to have great results on local hardware. - qwen 3.6 q6 - 450 K context using turboquant turbo3 mode llama.cpp fork - multimodal support This AI generated blog article is a kind…
Local LLMs are not as amazing as some people will lead you to believe (www.reddit.comhttps) Local LLMs are great, in fact lots of simpler things like a fastapi web server they can do quite well. The moment you move outside of that - things get a bit worse.
club-3090 adds experimental FP8 support for Qwen3.6-27B! (www.reddit.com via reddit) It’s finally here! Something many of us running dual RTX 3090 rigs have been anticipating.
why I have just installed OpenLumara, my first Agentic Framework. Using only local models, served by LMStudio (www.reddit.comhttps) Where I came across it: https://www.reddit.com/r/LocalLLaMA/comments/1txxgpq/openlumara_a_different_kind_of_ai_agent_written/ DISCLAIMER: A good posting would be: This is what I wanted to do with Lumara. Here is what worked, here is what d…
Qwen 3.6 27B on DeepSWE (www.reddit.com via reddit) Overview: It scored 2% (1.79% rounded up) It is 18/20th place scoring above Haiku 4.5 and Minimax M2.7 Full benchmark took 70 hours Average time per task 32m Average output tokens per task: 44k Perspectives: It scored suspiciously similar…
Why aren't languages/frameworks offering retrained models for their project? (news.ycombinator.com) The cost of training is coming down. We have incredible open source models (especially smaller ones) like qwen 3.6-27b.
Local agents on a MacBook Pro M5 finally feel practical to me (www.reddit.com via reddit) Realtime check X for new people to follow I have been pretty pessimistic about local models for agentic workflows for a while. Not because they were useless, but because in practice they often felt just a bit too slow, too fragile, or too…
Need some guidance toying with local models (www.reddit.com via reddit) Hi, so I have a pretty low-end laptop regarding running LLMs locally (NVIDIA GeForce RTX 3050 with 4GB VRAM, AMD Ryzen 7 5800H and 16GB DDR4) and while I'm not looking for anything to realistically work with, I'd be interested in how could…
Qwen 3.6 27B KV cache quant benchmarks: 75 pairs, q8/q6/q5/q4, KVarN, Turbo/TCQ (www.reddit.com via reddit) Full benchmark results and in-depth analysis are available in the articles: KV Cache Quantization Benchmarks for Long Context and KVarN KV Cache: Implementation and Benchmarks. BeeLlama.cpp (my llama.cpp fork) was used as inference engine…
Just received RTX 6000 Pro, have 5090- how would you use? (www.reddit.com via reddit) Just received an RTX 6000 PRO, and I have an 5090 Astral. I am considering running a Qwen 3.6 27B on the 5090 and maybe two or three more on the 6000 to play roles such as lead SWE and coder and researcher.
I can't wait for all the x250 sample distills of Mythos and GPT-5.6 (www.reddit.com via reddit) Just kidding. Are there any distills that actually improve a model's quality?
↯ Anthropic Mythos↯ Gemma 4↯ Qwen 3.6↯ Qwen 3.5mythosgpt-5gemma+1
Model recommendations for cybersecurity (www.reddit.com via reddit) My goal: I want to use an LLM to learn more about software/firmware reverse engineering and binary analysis. Eventually I would like to learn how to build agents to augment parts of this process.
Best Coding Harness for Qwen3.6 35B? (www.reddit.com via reddit) I've been happily using GitHub Copilot for 7-8 months, primarily in Visual Studio and VS Code, mostly with the built-in flagship models and have felt like the output is worth the cost. Lately I've been playing with a lot of different local…
Z.ai, we need Air! GLM GGUF wen? (www.reddit.com via reddit) First we never saw an upgraded Air model after 4.5. Then GLM 4.7 Turbo was great, but quickly surpassed for coding.