DeepSeek V4.1 — VRAM Requirements
Written by Jakub Rusinowski · Last updated June 26, 2026
How much GPU VRAM you need to run DeepSeek V4.1 DeepSeek V4.1 by DeepSeek locally, a 1600B-parameter model. Figures are quantized weights + KV cache + framework overhead, computed from the model's parameter count and published architecture — not a throughput model. See /en/methodology.
DeepSeek V4.1 needs about 967 GB VRAM at Q4_K_M.
VRAM by Quantization
| Quant | Bits/weight | Weights | Total VRAM |
|---|---|---|---|
| Q2_K | 2.63 | 526.0 GB | 526.8 GB |
| Q3_K_M | 3.41 | 682.0 GB | 682.8 GB |
| Q4_K_M | 4.83 | 966.0 GB | 966.8 GB |
| Q5_K_M | 5.67 | 1134.0 GB | 1134.8 GB |
| Q6_K | 6.56 | 1312.0 GB | 1312.8 GB |
| Q8_0 | 8.50 | 1700.0 GB | 1700.8 GB |
| F16 | 16.00 | 3200.0 GB | 3200.8 GB |
Switch quantization in the interactive calculator, or see the full DeepSeek V4.1 model page.
As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.
Add this badge to your model card
Model creators: paste this into your Hugging Face model card README to link readers straight to this VRAM breakdown.
[](https://llmconfigurator.com/en/vram-calculator/deepseek-v4-1-pro?utm_source=badge&utm_medium=referral&utm_campaign=readme_badge&utm_content=deepseek-v4-1-pro)Estimates only — actual VRAM varies with context length, batch size, runtime and KV-cache settings.