#qwen

A r/LocalLLaMA thread turned one user’s failed local tool-calling setup into a practical checklist: OpenWebUI, native tool calls, quants, runtimes and wrappers all matter.

r/LocalLLaMA cared because the numbers were concrete: 79 t/s on an RTX 5070 Ti with 128K context, tied to one llama.cpp flag choice.

The LocalLLaMA thread cared less about a release headline and more about which Qwen3.6 GGUF quant actually works. Unsloth’s benchmark post pushed the discussion into KLD, disk size, CUDA 13.2 failures, and the messy details that decide local inference quality.

Why it matters: Alibaba is putting a small-active-parameter multimodal coding model into open weights rather than keeping it API-only. The tweet says Qwen3.6-35B-A3B has 35B total parameters, 3B active parameters, and an Apache 2.0 license; the blog reports 73.4 on SWE-bench Verified and 51.5 on Terminal-Bench 2.0.

HN upvoted the joke because it exposed a real discomfort: one vivid SVG prompt can make a small local model look better than a flagship model, but nobody agrees what that proves.

LocalLLaMA upvoted this because it turns a messy GGUF choice into a measurable tradeoff. The post compares community Qwen3.5-9B quants against a BF16 baseline using mean KLD, then the comments push for better visual encoding, Gemma 4 runs, Thireus quants, and long-context testing.

HN latched onto the open-weight angle: a 35B MoE model with only 3B active parameters is interesting if it can actually carry coding-agent work. Qwen says Qwen3.6-35B-A3B improves sharply over Qwen3.5-35B-A3B, while commenters immediately moved to GGUF builds, Mac memory limits, and whether open-model-only benchmark tables are enough context.

LocalLLaMA reacted because the post attacks a very real pain point for running large MoE models on limited VRAM. The author tested a llama.cpp fork that tracks recently routed experts and keeps the hot ones in VRAM for Qwen3.5-122B-A10B, reporting 26.8% faster token generation than layer-based offload at a similar 22GB VRAM budget.

LocalLLaMA reacted because the joke-like idea of an LLM tuning its own runtime came with concrete benchmark numbers. The author says llm-server v2 adds --ai-tune, feeding llama-server help into a tuning loop that searches flag combinations and caches the fastest config; on their rig, Qwen3.5-27B Q4_K_M moved from 18.5 tok/s to 40.05 tok/s.

LocalLLaMA paid attention to this post because it looked like real engineering cleanup instead of another inflated speed screenshot. On April 13, 2026, the author said a stock-MLX baseline for Qwen3.5-9B at 2048 tokens improved from 30.96 tok/s to 127.07 tok/s, with 89.36% acceptance and the full runtime released as open source.

A detailed r/LocalLLaMA benchmark reports single- and dual-GPU numbers for Qwen3.5-27B int4 on Intel Arc Pro B70 32GB using Intel’s vLLM fork. The setup is still finicky, but the measurements outline a practical path for local serving on Intel hardware.

A LocalLLaMA implementation report says a native MLX DFlash runtime can speed up Qwen inference on Apple Silicon by more than 2x in several settings. The notable part is not only the throughput gain, but the claim that outputs remain bit-for-bit identical to the greedy baseline.