Models / Run locally
Run DeepSeek V4 Pro locally
DeepSeek · ≈671B-class MoE · open weights
Same server-class footprint as base V4 — this is datacenter hardware, not a workstation. If you want V4-family quality locally, start from the smaller community distillations.
By quantization
Hardware requirements
| Quantization | ≈ File size | ≈ Memory needed | Runs on |
|---|---|---|---|
| Q4_K_M (4-bit) | 389 GB | 447 GB | Multi-GPU servers (4–8× 80GB) or 512GB Mac Studio clusters |
| Q8_0 (8-bit) | 718 GB | 826 GB | Multi-node GPU clusters — datacenter serving only |
| FP16 (full) | 1409 GB | 1620 GB | Multi-node GPU clusters — datacenter serving only |
Sizes are computed from the ≈671B parameter count with standard GGUF math and ~15% runtime overhead — verify against the model card before buying hardware. As a mixture-of-experts model only ≈37B parameters are active per token, which helps speed — but the full weights still have to fit in memory.
Local setup
How to run it
Grab a community GGUF conversion from Hugging Face and serve it with llama.cpp, LM Studio, or (for multi-GPU rigs) vLLM with the original weights. Pick the quantization from the table above that fits your memory.
For serious throughput on server hardware, vLLM with tensor parallelism is the standard serving stack; llama.cpp and MLX (Apple silicon) win below that line.
The API route
Or skip the GPU entirely
The same model is one API call away at $0.6 / M input · $1.8 / M output — no download, no VRAM math, metered to the exact call. Free starter credits cover your first runs, and because the weights are open, nothing locks you in: start metered, move to your own hardware if sustained volume ever justifies it.
FAQ
Frequently asked questions
Can my GPU run DeepSeek V4 Pro?
DeepSeek V4 Pro is ≈671B-class MoE. At 4-bit quantization you need roughly 447GB of memory (multi-gpu servers (4–8× 80gb) or 512gb mac studio clusters). Same server-class footprint as base V4 — this is datacenter hardware, not a workstation. If you want V4-family quality locally, start from the smaller community distillations.
Is there a DeepSeek V4 Pro GGUF?
Open-weight releases in this family get community GGUF conversions on Hugging Face shortly after release — search the model name plus "GGUF" and pick the quantization your memory allows from the table above.
Is running DeepSeek V4 Pro locally cheaper than the API?
Only at sustained volume. The API price is $0.6 / M input · $1.8 / M output with no hardware, electricity, or ops cost — a machine that can serve this model costs more per month idle than most teams' entire inference bill. Prototype metered, self-host when utilization justifies it.
Can I use the outputs commercially?
Open weights; commercial use permitted (MIT-style model license).
Explore
Run other models locally
One key. Every model. Exact prices.
Prototype on DeepSeek V4 Pro with free starter credits while the weights download. Free starter credits included — no subscription required.