You can reduce DeepSeek-V3.2 (V3.2-Exp) memory usage mainly by combining the model’s built-in sparse attention with careful inference configuration: shorter effective context, quantization, and sharding across multiple GPUs. DeepSeek-V3.2-Exp is a 685B-parameter MoE model with about 37B active parameters per token, using FP8-weight quantization and a sparse attention mechanism called DeepSeek Sparse Attention (DSA) to cut long-context compute and memory compared to the dense V3.1-Terminus model.:contentReference[oaicite:0]{index=0} If you are consuming the hosted DeepSeek API, most of this is handled for you; your levers are mainly max_tokens, prompt length, and how aggressively you stuff context. If you are self-hosting with vLLM, SGLang, or similar, you can tune GPU parallelism and quantization to fit your hardware envelope while remaining stable.
At the model level, memory is dominated by (1) weights, and (2) KV cache for attention. Because V3.2-Exp is FP8-quantized with MoE routing (37B active), its weight footprint is closer to a ~30–40B dense model rather than a full 685B dense model.:contentReference[oaicite:1]{index=1} The KV cache still scales with sequence length and batch size, so the biggest dial for memory is effective context: if you do not need 128k tokens, clamp max_position_embeddings / max_model_len in your engine and aggressively truncate or summarize history. The technical report shows that DSA drops attention complexity from O(L²) to roughly O(L·k) for the main attention, where k is a small top-k set, giving real memory and speed savings for long sequences.:contentReference[oaicite:2]{index=2} On NVIDIA A100, where native FP8 isn’t available, some users report out-of-memory if they run BF16 linears without enough tensor parallelism or sharding; the Hugging Face discussion for V3.2-Exp suggests increasing model parallel degree beyond 8–16 slices to make it fit.:contentReference[oaicite:3]{index=3}
At the system level, you get more practical gains by redesigning your pipeline rather than only pushing GPU knobs. For RAG and agent workloads, move as much “memory” as possible into a vector database such as Milvus or Zilliz Cloud, and keep only the current question + a small retrieved context window in the model. Instead of feeding a 100k-token history, keep a compressed working set, re-query the vector store when needed, and let long-term state live in storage. Engines like vLLM’s PagedAttention and SGLang’s “DP attention” configuration for DeepSeek V3.2 are tuned to improve stability and throughput under large batches; use their recommended flags (e.g., --dp 8 --enable-dp-attention for SGLang) to avoid fragmentation and KV cache bloat.:contentReference[oaicite:4]{index=4} Combined with modest context limits (e.g., 8–16k for standard chat, 32–64k for RAG) and careful batching, you can usually squeeze V3.2-Exp into an A100 cluster without heroic engineering while still leveraging its long-context capabilities when truly needed.