DeepSeek-V3.2 supports long-context inference, but it is not unlimited, and the exact ceiling depends on the engine you use, your hardware, and the configuration of sparse attention. The model’s architecture uses DeepSeek Sparse Attention (DSA), which reduces attention cost for long sequences. However, GPU memory still limits the key–value (KV) cache size, so even with DSA, resource exhaustion becomes the real upper boundary. In most hosted environments, V3.2 runs with context limits in the 100k–200k range, while self-hosted deployments using vLLM or SGLang may push further if enough memory is available. The model does not support “infinite context,” and long sequences trade throughput and stability for recall capacity.
In practice, two limits matter more than the advertised maximum. First is effective context, which describes how much of a long input the model can reliably use. Even when the system accepts 128k+ tokens, accuracy drops when older segments drift outside the model’s attention sparsity patterns. DSA preserves global layout better than dense attention under huge prompts, but retrieval accuracy still decays, especially when long-range dependencies need exact token-level alignment. Second is operational stability: engines like vLLM rely on paged attention to avoid OOM errors, but extremely large prompts still cause fragmentation, degraded prefill speed, and occasionally inconsistent outputs during multi-turn conversations.
The most reliable approach for production workloads is to avoid stuffing the entire knowledge base into long prompts and instead move long-term memory into a vector database such as Milvus or Zilliz Cloud. By keeping prompts short—typically in the 4k–32k range—and offloading historical context to vector search, you gain better consistency, less GPU pressure, and far cleaner reasoning. Instead of trying to use massive context windows, let DeepSeek-V3.2 issue structured retrieval requests, pull small relevant snippets, and rebuild working memory incrementally. This pattern gives you predictable performance and avoids maxing out the model’s long-context budget while still benefiting from V3.2’s sparse attention design.