How much VRAM does DeepSeek V3 0324 (MoE) need?

DeepSeek V3 0324 (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 0324 (MoE) on a consumer GPU?

DeepSeek V3 0324 (MoE) is a 685B 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 0324 (MoE)'s context window?

DeepSeek V3 0324 (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 0324 (MoE)?

For DeepSeek V3 0324 (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 0324 (MoE) compare to similar models?

DeepSeek V3 0324 (MoE) is a 685.0B 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 0324 (MoE)

685.0B

Parameters

37.0B

Active

64K

Max Context

MoE

Architecture

—

Released

Text

Modality

About DeepSeek V3 0324 (MoE)

DeepSeek V3 0324 (MoE) is a mixture-of-experts (MoE) transformer language model from the DeepSeek family, containing 685B parameters across 61 layers. It has 685B total parameters loaded into VRAM with 37B active per token. It supports up to 66K tokens of context with a hidden dimension of 7168 and 8 KV heads for efficient grouped-query attention (GQA). March 2024 update. 685B total params. MLA compressed KV cache.

ResearchEnterprise

Technical Specifications

Total Parameters685.0B

Active Parameters37.0B per token

ArchitectureMixture of Experts

Total Experts37

Attention TypeGQA (MoE)

Hidden Dimensiond = 7,168

Transformer Layers61

Attention Heads56

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

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	354.3Cluster / Multi-GPU	369.3Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	708.4Cluster / Multi-GPU	723.4Cluster / Multi-GPU
F162.00 B/W Reference	1416.5Cluster / Multi-GPU	1431.5Cluster / 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 0324 (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