Model ID
Qwen/Qwen3-1.7B
Capabilities
Qwen3 1.7B
The Qwen3 1.7B is a compact yet high-capability Causal Language Model built on a 28-layer architecture with 1.4B non-embedding parameters, offering a strong balance between reasoning quality, throughput, and serving efficiency. It supports a native 32,768-token context window and incorporates Qwen3’s hybrid thinking design, enabling flexible operation across step-by-step reasoning tasks and fast, general-purpose dialogue.
About this model
The Qwen3 1.7B is a compact yet high-capability Causal Language Model built on a 28-layer architecture with 1.4B non-embedding parameters, offering a strong balance between reasoning quality, throughput, and serving efficiency. It supports a native 32,768-token context window and incorporates Qwen3’s hybrid thinking design, enabling flexible operation across step-by-step reasoning tasks and fast, general-purpose dialogue.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3-1.7B). 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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