Model ID
Qwen/Qwen3-4B-Instruct-2507
Capabilities
Qwen3 4B Instruct 2507
The Qwen3 4B Instruct 2507 is an instruction-aligned Causal Language Model built on a 36-layer architecture with 3.6B non-embedding parameters, optimized for high-quality instruction following and preference-aligned generation. Unlike the base Qwen3 models, this updated model operates exclusively in non-thinking mode and is tuned to deliver direct, well-structured responses without intermediate reasoning traces. It supports a native 262,144-token context window, enabling effective handling of very long documents, extended conversations, and large-context comprehension workloads.
About this model
The Qwen3 4B Instruct 2507 is an instruction-aligned Causal Language Model built on a 36-layer architecture with 3.6B non-embedding parameters, optimized for high-quality instruction following and preference-aligned generation. Unlike the base Qwen3 models, this updated model operates exclusively in non-thinking mode and is tuned to deliver direct, well-structured responses without intermediate reasoning traces. It supports a native 262,144-token context window, enabling effective handling of very long documents, extended conversations, and large-context comprehension workloads.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3-4B-Instruct-2507). 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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