Model ID
Qwen/Qwen3-4B
Capabilities
Qwen3 4B
The Qwen3 4B is a mid-scale Causal Language Model built on a 36-layer architecture with 3.6B non-embedding parameters, positioned as a balanced option for workloads that require stronger reasoning depth without sacrificing serving efficiency. It supports a native 32,768-token context window and can be extended up to 131,072 tokens using YaRN, enabling reliable performance on long-form documents, multi-step analysis, and extended conversational workflows. Incorporating Qwen3’s hybrid thinking design, the model adapts smoothly between deliberate reasoning tasks and fast, general-purpose generation.
About this model
The Qwen3 4B is a mid-scale Causal Language Model built on a 36-layer architecture with 3.6B non-embedding parameters, positioned as a balanced option for workloads that require stronger reasoning depth without sacrificing serving efficiency. It supports a native 32,768-token context window and can be extended up to 131,072 tokens using YaRN, enabling reliable performance on long-form documents, multi-step analysis, and extended conversational workflows. Incorporating Qwen3’s hybrid thinking design, the model adapts smoothly between deliberate reasoning tasks and fast, general-purpose generation.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3-4B). 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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