Model ID
Qwen/Qwen3.8-27B
Capabilities
Qwen3.8 27B
Qwen3.8 27B is a compact dense vision-language model designed for coding, professional work, research, and long-horizon agentic tasks. It features 27B parameters across 64 layers, with a 5,120 hidden size and 24 attention heads, using a hybrid architecture that combines Gated DeltaNet linear attention with full attention in a 3:1 pattern. The model includes a native vision encoder for image and video understanding, supports flexible reasoning control, and integrates Multi-Token Prediction. It provides a 262K-token native context window, extensible to 1M tokens.
About this model
Qwen3.8 27B is a compact dense vision-language model designed for coding, professional work, research, and long-horizon agentic tasks. It features 27B parameters across 64 layers, with a 5,120 hidden size and 24 attention heads, using a hybrid architecture that combines Gated DeltaNet linear attention with full attention in a 3:1 pattern. The model includes a native vision encoder for image and video understanding, supports flexible reasoning control, and integrates Multi-Token Prediction. It provides a 262K-token native context window, extensible to 1M tokens.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3.8-27B). Released under Apache 2.0.
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Requires vllm/vllm-openai:qwen38 or any standard vLLM image v0.27.0+ to support this model configuration.
Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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