Model ID
microsoft/Phi-4-mini-instruct
Capabilities
Phi 4 Mini Instruct
Phi 4 Mini Instruct is a lightweight dense transformer model designed for efficient multilingual reasoning, instruction-following, and agentic tool-calling in constrained environments. It features 3.8B parameters with 32 layers, 3,072 hidden size, and 24 attention heads with grouped-query attention using 8 key-value heads. The model supports a 128K token context window and uses long RoPE scaling for extended context handling. Trained on 5T tokens with supervised fine-tuning and direct preference optimization, it is optimized for low-latency inference and strong reasoning performance.
About this model
Phi 4 Mini Instruct is a lightweight dense transformer model designed for efficient multilingual reasoning, instruction-following, and agentic tool-calling in constrained environments. It features 3.8B parameters with 32 layers, 3,072 hidden size, and 24 attention heads with grouped-query attention using 8 key-value heads. The model supports a 128K token context window and uses long RoPE scaling for extended context handling. Trained on 5T tokens with supervised fine-tuning and direct preference optimization, it is optimized for low-latency inference and strong reasoning performance.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (microsoft/Phi-4-mini-instruct). Released under MIT. 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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