How much VRAM does DeepSeek V4-Pro (MoE) need?

DeepSeek V4-Pro (MoE) requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run DeepSeek V4-Pro (MoE) on a consumer GPU?

DeepSeek V4-Pro (MoE) is a 1600B parameter model and requires datacenter or multi-GPU setups. At Q4_K_M, it needs approximately 0.0 GB VRAM, which exceeds consumer GPU limits. It can run on H100 (80 GB) or A100 (80 GB) datacenter GPUs, or across multiple consumer GPUs with tensor parallelism.

What is DeepSeek V4-Pro (MoE)'s context window?

DeepSeek V4-Pro (MoE) supports a maximum context window of 1,048,576 tokens. The default context is 4,096 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for DeepSeek V4-Pro (MoE)?

For DeepSeek V4-Pro (MoE), start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does DeepSeek V4-Pro (MoE) compare to similar models?

DeepSeek V4-Pro (MoE) is a 1.6T MoE model from the DeepSeek family. Browse other DeepSeek models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

DeepSeekMoEMIT

DeepSeek V4-Pro (MoE)

1.6T

Parameters

49.0B

Active

1.0M

Max Context

MoE

Architecture

Apr 1, 2026

Released

Text

Modality

About DeepSeek V4-Pro (MoE)

DeepSeek V4-Pro is the April 2026 frontier model pushing to 1.6 trillion total parameters with 49B active per token. It introduces Dynamic Sparse Attention (DSA) and token compression for efficient 1M token context processing. At this scale it is cluster-class only — requiring over 800 GB VRAM at Q4_K_M. The architecture represents the bleeding edge of open-weight AI: 80 transformer layers, 8192 hidden dimension, and advanced load-balanced MoE routing. Primarily accessed via API, with open weights available for research and enterprise self-hosting.

Frontier ResearchEnterpriseAgenticCode

Technical Specifications

Total Parameters1.6T

Active Parameters49.0B per token

ArchitectureMixture of Experts

Total Experts256

Active Experts8 per token

Attention TypeDSA (Dynamic Sparse Attention) + Token Compression

Hidden Dimensiond = 8,192

Transformer Layers80

Attention Heads64

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingYaRN-extended RoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	195K ctx	1.0M ctx	1.0M ctx
Q4_K_M0.50 B/W ~97% of FP16	827.3Cluster / Multi-GPU	888.0Cluster / Multi-GPU	1132.2Cluster / Multi-GPU	1147.0Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	1654.3Cluster / Multi-GPU	1715.1Cluster / Multi-GPU	1959.2Cluster / Multi-GPU	1974.0Cluster / Multi-GPU
F162.00 B/W Reference	3308.4Cluster / Multi-GPU	3369.1Cluster / Multi-GPU	3613.2Cluster / Multi-GPU	3628.1Cluster / Multi-GPU

1.5B

Layers

Context

32K

DeepSeek R1 Distill Qwen 7B

Params

7.6B

Layers

Context

32K

Find the right GPU for DeepSeek V4-Pro (MoE)

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide