Model ID
Qwen/Qwen3-14B
Capabilities
Qwen3 14B
The Qwen3 14B is a large-scale Causal Language Model built on a 40-layer architecture with 13.2B non-embedding parameters, designed for workloads that require deeper reasoning capacity and higher generation fidelity across complex tasks. It supports a native 32,768-token context window and can be extended up to 131,072 tokens using YaRN, enabling effective processing of long-form documents, extended analytical prompts, and multi-stage conversational workflows. Leveraging Qwen3’s hybrid thinking design, the model transitions smoothly between deliberate, multi-step reasoning and efficient general-purpose generation.
About this model
The Qwen3 14B is a large-scale Causal Language Model built on a 40-layer architecture with 13.2B non-embedding parameters, designed for workloads that require deeper reasoning capacity and higher generation fidelity across complex tasks. It supports a native 32,768-token context window and can be extended up to 131,072 tokens using YaRN, enabling effective processing of long-form documents, extended analytical prompts, and multi-stage conversational workflows. Leveraging Qwen3’s hybrid thinking design, the model transitions smoothly between deliberate, multi-step reasoning and efficient general-purpose generation.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3-14B). 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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