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

DeepSeek V4-Flash (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-Flash (MoE) on a consumer GPU?

DeepSeek V4-Flash (MoE) is a 284B 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-Flash (MoE)'s context window?

DeepSeek V4-Flash (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-Flash (MoE)?

For DeepSeek V4-Flash (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-Flash (MoE) compare to similar models?

DeepSeek V4-Flash (MoE) is a 284.0B 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 V4-Flash (MoE)

284.0B

Parameters

13.0B

Active

1.0M

Max Context

MoE

Architecture

—

Released

Text

Modality

About DeepSeek V4-Flash (MoE)

DeepSeek V4-Flash (MoE) is a mixture-of-experts (MoE) transformer language model from the DeepSeek family, containing 284B parameters across 48 layers. It has 284B total parameters loaded into VRAM with 13B active per token. It supports up to 1.0M tokens of context with a hidden dimension of 6144 and 8 KV heads for efficient grouped-query attention (GQA). April 2026. 284B total / 13B active. 1M context. Economical V4 variant. High-memory server class.

ResearchEnterprise

Technical Specifications

Total Parameters284.0B

Active Parameters13.0B per token

ArchitectureMixture of Experts

Total Experts13

Attention TypeGQA (MoE)

Hidden Dimensiond = 6,144

Transformer Layers48

Attention Heads48

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	195K ctx	1.0M ctx	1.0M ctx
Q4_K_M0.50 B/W ~97% of FP16	147.0Cluster / Multi-GPU	183.4Cluster / Multi-GPU	329.9Cluster / Multi-GPU	338.8Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	293.8Cluster / Multi-GPU	330.2Cluster / Multi-GPU	476.7Cluster / Multi-GPU	485.6Cluster / Multi-GPU
F162.00 B/W Reference	587.4Cluster / Multi-GPU	623.8Cluster / Multi-GPU	770.3Cluster / Multi-GPU	779.2Cluster / 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-Flash (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