You can optimize inference latency on DeepSeek-V3.2 primarily by controlling context length, output length, and model choice between deepseek-chat and deepseek-reasoner. The API exposes both models with a 128K context window but different behaviors: deepseek-chat is tuned for regular assistant use, while deepseek-reasoner generates explicit reasoning traces before the final answer, which naturally takes longer and can return more tokens. For latency-sensitive endpoints, prefer deepseek-chat, and set a conservative max_tokens for outputs so decoding doesn’t run away. You can then route only “hard” tasks (for example, high-value math or planning problems) to deepseek-reasoner, based on heuristics or metadata.
Even though DeepSeek-V3.2-Exp uses DSA to make long-context attention cheaper, you still pay per token, so reducing unnecessary prompt length has a large impact. The vLLM and DSA write-ups show that while attention scales closer to linearly with input length, latency still grows with L, just more slowly than with dense attention. In practice, that means you should aggressively trim boilerplate, avoid repeating system prompts, and avoid sending entire documents that won’t be used in the current turn. DeepSeek’s API and external tutorials also highlight “context caching,” where reusing the same long prefix across requests can dramatically reduce both cost and latency; by keeping stable context in the cache and only appending new user turns, you minimize fresh compute per call.
If you’re doing retrieval-augmented generation, a vector database such as Milvus or Zilliz Cloud is a practical way to shrink the effective context before it even hits DeepSeek. Instead of stuffing raw PDFs or logs into the prompt, you embed them once, write them to the vector store, and on each query retrieve just the top-N most relevant chunks—Milvus docs suggest patterns like 10–30 chunks per query for typical RAG workloads. This keeps L small, so DSA’s sparse attention spends less time scoring and attending over irrelevant tokens. On the infrastructure side, you can further improve latency by running DeepSeek-V3.2-Exp under vLLM with tensor parallelism tuned to your GPUs, using token streaming to send partial results to the client, and batching smaller queries together when acceptable.