Flagship-Level Coding in a 27B Dense Model

Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model (via) Big claims from Qwen about their latest open weight model:

Qwen3.6-27B delivers flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B (397B total / 17B active MoE) across all major coding benchmarks.

On Hugging Face Qwen3.5-397B-A17B is 807GB, this new Qwen3.6-27B is 55.6GB.

I tried it out with the 16.8GB Unsloth Qwen3.6-27B-GGUF:Q4_K_M quantized version and llama-server using this recipe by benob on Hacker News, after first installing llama-server using brew install llama.cpp:

llama-server         
    -hf unsloth/Qwen3.6-27B-GGUF:Q4_K_M 
    --no-mmproj 
    --fit on 
    -np 1 
    -c 65536 
    --cache-ram 4096 -ctxcp 2 
    --jinja 
    --temp 0.6 
    --top-p 0.95 
    --top-k 20 
    --min-p 0.0 
    --presence-penalty 0.0 
    --repeat-penalty 1.0 
    --reasoning on 
    --chat-template-kwargs '{"preserve_thinking": true}'

Here’s the transcript for “Generate an SVG of a pelican riding a bicycle”. This is an outstanding result for a 16.8GB local model:

Performance numbers reported by llama-server:

• Reading: 20 tokens, 0.4s, 54.32 tokens/s
• Generation: 4,444 tokens, 2min 53s, 25.57 tokens/s