DeepSeek V3.2 Exp cost & VRAM calculator
What does DeepSeek V3.2 Exp cost to run for your workload, and can you run it on your own hardware? Set your workload below - we compute per-provider API cost live and tell you honestly whether local hardware can run it.
This is where owning stops making sense
DeepSeek V3.2 Exp is a 685-billion-parameter model. No rig an individual can buy runs it - so unlike a workstation GPU model, there's no break-even to compute. The honest answer for nearly everyone is the per-provider API cost below.
685B total, ~37B active per token (MoE, 256 routed experts) - shares the V3 backbone. Adds DeepSeek Sparse Attention (DSA): a lightning indexer (FP8, Hadamard dot-product) selects top-2048 tokens per query, then MLA runs only over those. The first fine-grained sparse attention from DeepSeek and the precursor to V4’s CSA. Built on V3.1-Terminus. Experimental; superseded by V4 within ~7 months. Open weights under MIT.
Your workload
API cost for your workload
| Provider | Rate ($/1M) | Monthly cost |
|---|---|---|
| SiliconFlow | $0.27 in · $0.41 out | $0.01 cheapest |
Monthly cost is an estimate from list prices and your workload - verify against the provider before committing. Cached fraction applies the cache rate to that share of input.
Can you run it locally?
DeepSeek V3.2 Exp has no published quantization that fits a rig one person can buy, so there's no local-hardware recommendation and no break-even to compute. The honest answer is the per-provider API cost above.
685B total, ~37B active per token (MoE, 256 routed experts) - shares the V3 backbone. Adds DeepSeek Sparse Attention (DSA): a lightning indexer (FP8, Hadamard dot-product) selects top-2048 tokens per query, then MLA runs only over those. The first fine-grained sparse attention from DeepSeek and the precursor to V4’s CSA. Built on V3.1-Terminus. Experimental; superseded by V4 within ~7 months. Open weights under MIT.
See the model card for the full architecture notes and any cloud subscription plans.