How Much VRAM to Run DeepSeek V3?
DeepSeek V3 is a 671B MoE model with 37B active parameters per forward pass. FP16 weights require ~670GB — effectively impossible on consumer hardware. Q4_K_M brings weights to ~380GB, still requiring a multi-GPU cluster. For single-GPU use, run via API (Together, Fireworks, DeepSeek) or use the distilled 7B/8B variants.
Most users should access DeepSeek V3 via API rather than self-hosting. The model is exceptionally capable and the API pricing ($0.27/M input) makes it cost-competitive with self-hosting at most volumes.
VRAM requirements by quantization level
| Quantization | VRAM needed | Quality | Fits on |
|---|---|---|---|
| FP16 | 671GB | Full quality | 8×H100 cluster |
| Q8_0 | 335GB | Near-lossless | 4×H100 80GB |
| Q4_K_M | 168GB | Good | 8×RTX 4090 · 2×A100 80GB |
GPU recommendations & rental prices
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Frequently Asked Questions
Can I self-host DeepSeek V3 on consumer hardware?
Practically no. At 671B parameters, even Q4_K_M requires ~168GB VRAM — eight RTX 4090s or two A100 80GBs. For solo developers, the API at $0.27/M tokens is vastly more practical.
What is the distilled version of DeepSeek V3?
DeepSeek releases distilled versions (1.5B, 7B, 8B, 14B, 32B, 70B) derived from the V3/R1 training pipeline. The 7B and 14B variants run on a single consumer GPU and retain surprising capability.
Data verified 2026-08-20. GPU rental prices change frequently — verify before purchasing. See methodology.