Can You Run DeepSeek V4 Pro (1.6T MoE)? VRAM Requirements
DeepSeek V4 Pro (1.6T MoE) has 1.6T total parameters (MoE, 50B active per token). Running it at Q4_K_M (GGUF) with 16K context takes roughly 983 GB of memory (909 GB weights + 1.1 GB KV cache + runtime overhead). The minimum viable hardware is about 6× B200 192GB. All numbers are planning estimates, verified 2026-09-05.
⚠ Some architecture details of this model are estimated — treat results as ballpark figures.
VRAM by quantization (16K context)
| Quantization | Weights | Total needed | Minimum hardware |
|---|---|---|---|
| FP16 / BF16 | 2.91 TB | 3.14 TB | 19× B200 192GB |
| FP8 | 1.46 TB | 1.57 TB | 10× B200 192GB |
| Q8_0 (GGUF) | 1.54 TB | 1.67 TB | 10× B200 192GB |
| Q6_K (GGUF) | 1.19 TB | 1.29 TB | 8× B200 192GB |
| Q5_K_M (GGUF) | 1.03 TB | 1.12 TB | 7× B200 192GB |
| MXFP4 | 790 GB | 854 GB | 5× B200 192GB |
| NVFP4 | 790 GB | 854 GB | 5× B200 192GB |
| Q4_K_M (GGUF)recommended | 909 GB | 983 GB | 6× B200 192GB |
| Q3_K_M (GGUF) | 700 GB | 757 GB | 5× B200 192GB |
| Q2_K (GGUF) | 522 GB | 564 GB | 8× H100 80GB |
How context length changes memory (Q4_K_M (GGUF))
| Context | KV cache | Total needed |
|---|---|---|
| 4K | 0.3 GB | 982 GB |
| 16K | 1.1 GB | 983 GB |
| 64K | 4.3 GB | 986 GB |
| 128K | 8.6 GB | 990 GB |
| 256K | 17.2 GB | 999 GB |
| 1M | 65.4 GB | 1.02 TB |
KV cache grows linearly with context; FP8 KV cache halves it again (try it in the calculator below).
Customize the estimate
LLM VRAM Calculator — Can I Run It?
Estimate how much GPU memory (VRAM) you need to run open-weight LLMs like Kimi K3, DeepSeek R1, Qwen3, or Llama locally. Pick a model, quantization, and context length — the calculator adds up weights, KV cache, and runtime overhead, then shows which GPUs or Macs can fit it. Runs entirely in your browser: no upload, no signup.
1.6T total params · 50B active (MoE) · max 1M context · specs partially estimated
Recommended balance · ~0.61 bytes/param
Estimated memory needed
990 GB
Architecture details for this model are estimated — treat results as a ballpark.
Will it fit?
Assumes ~92% of device memory is usable. Multi-GPU counts are for tensor/pipeline parallel serving (vLLM, SGLang); Apple Silicon uses unified memory via llama.cpp or MLX.
FAQ
How much VRAM does DeepSeek V4 Pro (1.6T MoE) need?
About 983 GB at the recommended Q4_K_M (GGUF) quantization with 16K context: 909 GB for weights, 1.1 GB for KV cache, plus runtime overhead. Longer context grows the KV cache.
What is the minimum hardware for DeepSeek V4 Pro (1.6T MoE)?
Roughly 6× B200 192GB, assuming 92% of device memory is usable. Lower-end setups can try more aggressive quantization (Q3/Q2) at a noticeable quality cost.
Can I run DeepSeek V4 Pro (1.6T MoE) on a Mac?
Not realistically. Even at Q4_K_M (GGUF) it needs about 983 GB, which exceeds the largest Mac M3 Ultra 512GB. Use a multi-GPU server or an API instead.
Which quantization should I use for DeepSeek V4 Pro (1.6T MoE)?
Q4_K_M is the recommended balance of size and quality; with headroom, Q6_K or Q8_0 reduce quality loss further.