Model ID
deepseek-ai/DeepSeek-V3.2
Capabilities
DeepSeek V3.2
DeepSeek V3.2 is a large Mixture-of-Experts (MoE) language model that balances high computational efficiency with exceptional reasoning and agent capabilities. The model features a 685B-class architecture with ~37B activated parameters, built on a 61-layer transformer with 128 attention heads and a 7,168 hidden size, utilizing 256 routed experts with 8 experts activated per token. It integrates DeepSeek Sparse Attention (DSA) to significantly reduce computational complexity while maintaining long-context performance, supporting up to a ~160K token context window.
About this model
DeepSeek V3.2 is a large Mixture-of-Experts (MoE) language model that balances high computational efficiency with exceptional reasoning and agent capabilities. The model features a 685B-class architecture with ~37B activated parameters, built on a 61-layer transformer with 128 attention heads and a 7,168 hidden size, utilizing 256 routed experts with 8 experts activated per token. It integrates DeepSeek Sparse Attention (DSA) to significantly reduce computational complexity while maintaining long-context performance, supporting up to a ~160K token context window.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (deepseek-ai/DeepSeek-V3.2). Released under MIT. Further reading: Paper · Blog
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Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on MI325X · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
AMD Instinct MI325X
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