Model ID
deepseek-ai/DeepSeek-V3.2-Speciale
Capabilities
DeepSeek V3.2 Speciale
DeepSeek V3.2 Speciale is a reasoning-focused Mixture-of-Experts (MoE) large language model developed for advanced mathematical reasoning, coding, and complex analytical tasks. The model features a 685B parameter 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 supports up to a ~160K token context window and integrates DeepSeek Sparse Attention (DSA) to reduce computational complexity while maintaining performance.
About this model
DeepSeek V3.2 Speciale is a reasoning-focused Mixture-of-Experts (MoE) large language model developed for advanced mathematical reasoning, coding, and complex analytical tasks. The model features a 685B parameter 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 supports up to a ~160K token context window and integrates DeepSeek Sparse Attention (DSA) to reduce computational complexity while maintaining performance.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (deepseek-ai/DeepSeek-V3.2-Speciale). 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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