How much VRAM does DeepSeek V3 (MoE) need?

DeepSeek V3 (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 V3 (MoE) on a consumer GPU?

DeepSeek V3 (MoE) is a 671B 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 V3 (MoE)'s context window?

DeepSeek V3 (MoE) supports a maximum context window of 65,536 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 V3 (MoE)?

For DeepSeek V3 (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 V3 (MoE) compare to similar models?

DeepSeek V3 (MoE) is a 671.0B 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 V3 (MoE)

671.0B

Parameters

37.0B

Active

64K

Max Context

MoE

Architecture

Dec 26, 2024

Released

Text

Modality

About DeepSeek V3 (MoE)

DeepSeek V3 is the non-reasoning base model sharing R1's architecture — 671B total MoE with 37B active per token using MLA for compressed KV cache. It delivers frontier-class general performance on par with GPT-4o and Claude 3.5 Sonnet. MIT licensed, making it the most capable truly open-weight model available. Like R1, the full model requires server/cluster hardware (~370 GB at Q4_K_M), but its architecture innovations (MLA, auxiliary-loss-free load balancing, multi-token prediction) have influenced the entire field.

General PurposeCodeResearchEnterprise

Technical Specifications

Total Parameters671.0B

Active Parameters37.0B per token

ArchitectureMixture of Experts

Total Experts256

Active Experts8 per token

Attention TypeMLA (Multi-head Latent Attention)

Hidden Dimensiond = 7,168

Transformer Layers61

Attention Heads56

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	64K ctx
Q4_K_M0.50 B/W ~97% of FP16	347.1Cluster / Multi-GPU	362.1Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	693.9Cluster / Multi-GPU	708.9Cluster / Multi-GPU
F162.00 B/W Reference	1387.6Cluster / Multi-GPU	1402.6Cluster / Multi-GPU

1.5B

Layers

Context

32K

DeepSeek R1 Distill Qwen 7B

Params

7.6B

Layers

Context

32K

Find the right GPU for DeepSeek V3 (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