DeepSeek·DeepSeek·DeepseekV4ForCausalLM

DeepSeek V4 Flash — Hardware Requirements & GPU Compatibility

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DeepSeek V4 Flash is a 158.1B-parameter open language model from DeepSeek in the DeepSeek family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 95.16 GB of VRAM — see which GPUs and Macs can run it below.

3.4M downloads 1.4K likes1049K context

Specifications

Publisher
DeepSeek
Family
DeepSeek
Parameters
158.1B
Architecture
DeepseekV4ForCausalLM
Context Length
1,048,576 tokens
Vocabulary Size
129,280
Release Date
2026-05-06
License
MIT

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How Much VRAM Does DeepSeek V4 Flash Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
IQ2_XXS2.2043.8 GB
IQ2_XS2.4047.7 GB
Q2_K3.4067.5 GB
Q3_K_M3.9077.4 GB
Q4_K_M4.8095.2 GB
Q5_K_M5.70113.0 GB
Q6_K6.60130.7 GB
Q8_08.00158.4 GB

Which GPUs Can Run DeepSeek V4 Flash?

Q4_K_M · 95.2 GB

DeepSeek V4 Flash (Q4_K_M) requires 95.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 124+ GB is recommended. Using the full 1049K context window can add up to 11.5 GB, bringing total usage to 106.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run DeepSeek V4 Flash?

Q4_K_M · 95.2 GB

5 devices with unified memory can run DeepSeek V4 Flash, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (128 GB).

Related Models

Frequently Asked Questions

How much VRAM does DeepSeek V4 Flash need?

DeepSeek V4 Flash requires 95.2 GB of VRAM at Q4_K_M, or 158.4 GB at Q8_0. Full 1049K context adds up to 11.5 GB (106.7 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 158.1B × 4.8 bits ÷ 8 = 94.8 GB

KV Cache + Overhead 0.4 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 11.9 GB (at full 1049K context)

VRAM usage by quantization

95.2 GB
106.7 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run DeepSeek V4 Flash?

No — DeepSeek V4 Flash requires at least 43.8 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for DeepSeek V4 Flash?

For DeepSeek V4 Flash, Q4_K_M (95.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (113.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 43.8 GB.

VRAM requirement by quantization

IQ2_XXS
43.8 GB
Q2_K
67.5 GB
Q4_K_M
95.2 GB
Q5_K_M
113.0 GB
Q6_K
130.7 GB
Q8_0
158.4 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run DeepSeek V4 Flash on a Mac?

DeepSeek V4 Flash requires at least 43.8 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run DeepSeek V4 Flash locally?

Yes — DeepSeek V4 Flash can run locally on consumer hardware. At Q4_K_M quantization it needs 95.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is DeepSeek V4 Flash?

At Q4_K_M, DeepSeek V4 Flash can reach ~31 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: AMD Instinct MI300X5300 ÷ 95.2 × 0.55 = ~31 tok/s

Estimated speed at Q4_K_M (95.2 GB)

~31 tok/s
~19 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of DeepSeek V4 Flash?

At Q4_K_M, the download is about 94.84 GB. The full-precision Q8_0 version is 158.07 GB. The smallest option (IQ2_XXS) is 43.47 GB.

Which GPUs can run DeepSeek V4 Flash?

No single consumer GPU has enough VRAM to run DeepSeek V4 Flash at Q4_K_M (95.2 GB). Multi-GPU or professional hardware is required.

Which devices can run DeepSeek V4 Flash?

5 devices with unified memory can run DeepSeek V4 Flash at Q4_K_M (95.2 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.