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How to Deploy DeepSeek V4 Flash Locally

DeepSeek V4 Flash is a MoE model with 284B total parameters and 13B active per token. At Q4 it needs 160GB of VRAM to sit entirely on the GPU; with experts offloaded to system RAM it needs only 7GB of VRAM plus 155GB of RAM. The cheapest card that runs it today is the Mac Studio M3 Ultra 512GB at about $22,000, at roughly 47.2 tokens/s. In offload mode the cheapest card that runs it is the Tesla V100 16GB, at roughly 12 tokens/s.

DeepSeek V4 Flash details

Launch dateJul 31, 2026
Revision0731
LicenseMIT
ArchitectureMoE
Ollama tag
Min VRAM at Q4160 GB · Q4_K_M · 8K
Experts offloaded7 GB VRAM + 155 GB RAM
Parameters284B
MoE13B
Layers43
Hidden size4,096
KV heads1
Head dim512
Max context1M
VendorDeepSeek

How much VRAM does DeepSeek V4 Flash need for local deployment?

MoE: all 284B parameters must stay resident in memory, while speed is set by the 13B active per token.

QuantizationWeightsGBTotal at 8KGBTotal at 32KGBTotal at 128KGB
1-bit86.9GGUF68.370.378.4
2-bit96.8GGUF107.2109.2117.3
3-bit128.2GGUF137.8139.8147.8
4-bit155.1GGUF160162170.1
5-bit191.6est.193.3195.3203.4
8-bit161.9GGUF296.1298.1306.1
FP16555.4est.557.1559.1567.2

Total = weights + KV cache (FP16) + 1GB runtime overhead. Weights marked GGUF are measured file sizes from the repository; the rest are estimated from bytes per parameter. Contexts beyond this model's 1M limit show a dash. Each tier takes whichever file of that bit width the repository has, preferring unsloth dynamic quants, so the exact file name varies by model.

Which GPUs can run DeepSeek V4 Flash?

eBay
DeviceVRAMGBPriceEst. t/sSuggested context
Mac Studio M3 Ultra 512GB512$22,00011 listings47.2128K

Fits when weights + KV cache + runtime overhead is at or below usable memory, single card. "Offload" on MoE models means it runs with expert weights in system RAM (only attention layers and KV cache stay in VRAM); that speed assumes 70 GB/s RAM bandwidth. The context column is the largest tier that fits.

Recommended GPUs for DeepSeek V4 Flash with CPU/GPU offloading

eBay

Expert weights live in system RAM (needs at least 155 GB); VRAM holds only attention layers, shared experts and the KV cache, at least 7 GB at Q4. Speed is bound by RAM bandwidth (estimated at 70 GB/s) and is far slower than a full-VRAM setup.

DeviceVRAMGBPriceRAM neededGBEst. t/s
Tesla V100 16GB16$30515511.6
Radeon RX 6950 XT16$50015510.7
GeForce RTX 2080 Ti 22GB22$55115511.2
Radeon RX 7800 XT16$56815510.8
GeForce RTX 5060 Ti 16GB16$70015510.8
Radeon RX 7900 XT20$70015511.1
Tesla V100 32GB32$72015511.6
Instinct MI50 32GB32$76815511.4
GeForce RTX 507012$77515511.3
Radeon RX 9070 XT16$79515510.8
Radeon RX 7900 XTX24$99915511.4
Instinct MI10032$99915511.6
GeForce RTX 5070 Ti16$1,20015511.6
GeForce RTX 408016$1,26315511.4
GeForce RTX 309024$1,55015511.7
GeForce RTX 508016$1,80015511.7
RTX PRO 4000 Blackwell24$2,95015511.3
GeForce RTX 409024$3,19915511.7
Radeon PRO W790048$3,49515511.2
RTX PRO 4500 Blackwell32$4,30015511.6
A100 40GB PCIe40$4,89915512
Instinct MI21064$5,04615511.8
MacBook Pro M4 Max 128GB128$5,54915510.7
GeForce RTX 509032$6,50015512.1
Mac Studio M4 Max 128GB128$6,54715510.7
MacBook Pro M5 Max 128GB128$7,25015510.9
RTX PRO 6000 Blackwell96$16,98515512.1
Arc Pro B602415510.1
GeForce RTX 5090 D V22415511.9
Arc Pro B653215510.6
Arc Pro B703215510.6
GeForce RTX 4080 SUPER 32GB3215511.4
GeForce RTX 5090 D3215512.1
Radeon AI PRO R97003215510.8
Radeon PRO W78003215510.7
CMP 170HX 40GB4015512
GeForce RTX 4090 48GB4815511.7
RTX PRO 5000 Blackwell 48GB4815511.9
CMP 170HX 64GB6415512
RTX PRO 5000 Blackwell 72GB7215511.9
RTX PRO 6000D8415512
DGX Spark 128GB1281559.9
Mac Studio M5 Max 128GB12815510.9
Ryzen AI Max+ 395 128GB1281559

Deploy DeepSeek V4 Flash locally with Ollama, llama.cpp or vLLM

Ollama

The Ollama library has no official tag for it yet.

llama.cpp

llama.cpp pulls the GGUF straight from Hugging Face; -c sets the context length.

llama-server -hf unsloth/DeepSeek-V4-Flash-0731-GGUF:Q4_K_M -c 8192
vLLM

vLLM serves the original-precision weights, which needs far more memory than GGUF and usually more than one GPU.

vllm serve deepseek-ai/DeepSeek-V4-Flash-0731

File names follow that week's model-library snapshot; the definitions are in the methodology. Methodology

FAQ

How much VRAM does DeepSeek V4 Flash need?

At Q4_K_M with an 8K context DeepSeek V4 Flash needs about 160GB of VRAM; 192GB leaves comfortable headroom.

What is the cheapest GPU that runs DeepSeek V4 Flash?

The Mac Studio M3 Ultra 512GB: 512GB of VRAM, currently about $22,000, at roughly 47.2 tokens/s.

Can you run DeepSeek V4 Flash with Ollama?

Not yet: the Ollama library has no official tag for it. You can load the GGUF with llama.cpp instead.