Model ID
Qwen/Qwen3.6-27B
Capabilities
Qwen3.6 27B
Qwen3.6 27B is a dense multimodal transformer model designed for high-performance agentic coding and long-context reasoning. It features 27B parameters with 64 layers, 5,120 hidden size, and 24 attention heads with 4 key-value heads, combining linear and full attention. The model supports a 262K token context window, extendable to ~1M tokens, and includes a 27-layer vision encoder. Built as the first open-weight Qwen3.6 dense variant, it delivers strong coding performance without MoE complexity.
About this model
Qwen3.6 27B is a dense multimodal transformer model designed for high-performance agentic coding and long-context reasoning. It features 27B parameters with 64 layers, 5,120 hidden size, and 24 attention heads with 4 key-value heads, combining linear and full attention. The model supports a 262K token context window, extendable to ~1M tokens, and includes a 27-layer vision encoder. Built as the first open-weight Qwen3.6 dense variant, it delivers strong coding performance without MoE complexity.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3.6-27B). Released under Apache 2.0. Further reading: Blog
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Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Llama.cpp
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on MI325X · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
AMD Instinct MI325X
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