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How to Deploy DeepSeek V3.2 Locally

DeepSeek V3.2 is a MoE model with 671B total parameters and 37B active per token. At Q4 it needs 376GB of VRAM to sit entirely on the GPU; with experts offloaded to system RAM it needs only 12GB of VRAM plus 365GB of RAM. The cheapest device that holds it is the Mac Studio M3 Ultra 512GB, with no price data yet, at roughly 19.4 tokens/s. In offload mode the cheapest card that runs it is the Tesla V100 16GB, at roughly 4 tokens/s.

DeepSeek V3.2 details

Launch dateDec 1, 2025
Hugging Face repodeepseek-ai/DeepSeek-V3.2
Revision
LicenseMIT
ArchitectureMoE
GGUF repo
Ollama tag
Min VRAM at Q4376 GB · Q4_K_M · 8K
Experts offloaded12 GB VRAM + 365 GB RAM
Parameters671B
MoE37B
Layers61
Hidden size7,168
KV heads128
Head dim192
Max context160K
VendorDeepSeek

How much VRAM does DeepSeek V3.2 need for local deployment?

MoE: all 671B parameters must stay resident in memory, while speed is set by the 37B active per token.

QuantizationWeightsGBTotal at 8KGBTotal at 32KGBTotal at 128KGB
1-bit157.5est.159160.6167.1
2-bit249.3est.250.9252.5258.9
3-bit321.5est.323.1324.7331.1
4-bit374est.375.5377.2383.6
5-bit452.8est.454.3455.9462.3
8-bit695.5est.697.1698.7705.1
FP161,312.3est.1,313.91,315.51,321.9

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 160K 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 V3.2?

AmazonJP
DeviceVRAMGBPriceEst. t/sSuggested context
Mac Studio M3 Ultra 512GB512No listings19.4128K

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 V3.2 with CPU/GPU offloading

AmazonJP

Expert weights live in system RAM (needs at least 365 GB); VRAM holds only attention layers, shared experts and the KV cache, at least 12 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$6593653.9
Radeon RX 9070 XT16$8843653.7
GeForce RTX 5060 Ti 16GB16$9243653.7
GeForce RTX 507012$9563653.8
Radeon RX 7800 XT16$9663653.7
Tesla V100 32GB32$1,1413653.9
Arc Pro B6024$1,1643653.5
Radeon RX 7900 XT20$1,2443653.8
Radeon RX 6950 XT16$1,4043653.6
GeForce RTX 5070 Ti16$1,5763653.9
Radeon RX 7900 XTX24$1,7383653.8
GeForce RTX 508016$1,8683653.9
Arc Pro B7032$2,0743653.6
GeForce RTX 408016$2,1873653.8
Radeon AI PRO R970032$2,2443653.7
GeForce RTX 309024$2,5203653.9
Ryzen AI Max+ 395 128GB128$3,9133653.1
RTX PRO 4000 Blackwell24$3,9563653.8
GeForce RTX 409024$4,9053653.9
GeForce RTX 509032$7,4893654
DGX Spark 128GB128$8,2233653.4
GeForce RTX 2080 Ti 22GB223653.8
GeForce RTX 5090 D V2243654
Arc Pro B65323653.6
GeForce RTX 4080 SUPER 32GB323653.8
GeForce RTX 5090 D323654
Instinct MI100323653.9
Instinct MI50 32GB323653.8
Radeon PRO W7800323653.6
RTX PRO 4500 Blackwell323653.9
A100 40GB PCIe403654
CMP 170HX 40GB403654
GeForce RTX 4090 48GB483653.9
Radeon PRO W7900483653.8
RTX PRO 5000 Blackwell 48GB483654
CMP 170HX 64GB643654
Instinct MI210643653.9
RTX PRO 5000 Blackwell 72GB723654
RTX PRO 6000D843654
RTX PRO 6000 Blackwell963654
Mac Studio M4 Max 128GB1283653.6
Mac Studio M5 Max 128GB1283653.7
MacBook Pro M4 Max 128GB1283653.6
MacBook Pro M5 Max 128GB1283653.7

Deploy DeepSeek V3.2 locally with Ollama, llama.cpp or vLLM

Ollama

The Ollama library has no official tag for it yet.

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-V3.2

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

FAQ

How much VRAM does DeepSeek V3.2 need?

At Q4_K_M with an 8K context DeepSeek V3.2 needs about 376GB of VRAM; 452GB leaves comfortable headroom.

What is the cheapest GPU that runs DeepSeek V3.2?

The Mac Studio M3 Ultra 512GB: 512GB of VRAM at roughly 19.4 tokens/s. We have no price data for it yet.

Can you run DeepSeek V3.2 with Ollama?

Not yet: there is no official Ollama tag and no public GGUF build for it.