Model ID
Qwen/Qwen3-32B
Capabilities
Qwen3 32B
The Qwen3 32B is the largest dense Causal Language Model in the Qwen3 family, built on a 64-layer architecture with 31.2B non-embedding parameters to support high-complexity reasoning and high-fidelity generation at scale. It provides a native 32,768-token context window and can be extended up to 131,072 tokens using YaRN, enabling robust handling of long-form documents, extended analytical chains, and deeply nested multi-turn interactions. Incorporating Qwen3’s hybrid thinking design, the model balances deliberate multi-step reasoning with efficient general-purpose generation.
About this model
The Qwen3 32B is the largest dense Causal Language Model in the Qwen3 family, built on a 64-layer architecture with 31.2B non-embedding parameters to support high-complexity reasoning and high-fidelity generation at scale. It provides a native 32,768-token context window and can be extended up to 131,072 tokens using YaRN, enabling robust handling of long-form documents, extended analytical chains, and deeply nested multi-turn interactions. Incorporating Qwen3’s hybrid thinking design, the model balances deliberate multi-step reasoning with efficient general-purpose generation.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3-32B). Released under Apache 2.0. Further reading: Paper · Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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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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