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DeepSeek-V3.1 VRAM Requirements

DeepSeek, China · Released August 21, 2025

DeepSeek-V3.1 has 684.5B parameters (40B active per token). In BF16 its weights alone take 1250 GiB; quantized to 4-bit, about 378 GiB. Each 32K-token request adds 2.14 GiB of KV cache. Cheapest way to run it today: 6 × NVIDIA A100 80GB at $5.60 per hour.

Size your own setup in the calculator →

VRAM by precision and context length

Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.

Weights formatWeights only+ 8K context+ 32K context+ 128K context+ 160K context
FP8 (native)627627629636638
BF16 / FP1612501250125212591261
FP8 / INT8627627629635637
4-bit (GGUF Q4_K_M)378378380386388

DeepSeek-V3.1 GGUF files

Exact size of each quantization in unsloth/DeepSeek-V3.1-GGUF (opens in a new tab), and the smallest GPU that runs it with llama.cpp, Ollama or LM Studio and an 8K-token context.

QuantizationFile sizeSmallest GPU
BF161250 GiBMore than 96 GB: see the calculator
UD-Q8_K_XL727 GiBMore than 96 GB: see the calculator
Q8_0664 GiBMore than 96 GB: see the calculator
UD-Q6_K_XL535 GiBMore than 96 GB: see the calculator
Q6_K513 GiBMore than 96 GB: see the calculator
UD-Q5_K_XL451 GiBMore than 96 GB: see the calculator
Q5_K_M443 GiBMore than 96 GB: see the calculator
Q5_K_S431 GiBMore than 96 GB: see the calculator
Q4_1392 GiBMore than 96 GB: see the calculator
Q4_K_M378 GiBMore than 96 GB: see the calculator
UD-Q4_K_XL360 GiBMore than 96 GB: see the calculator
Q4_K_S355 GiBMore than 96 GB: see the calculator
Q4_0354 GiBMore than 96 GB: see the calculator
IQ4_NL353 GiBMore than 96 GB: see the calculator
IQ4_XS334 GiBMore than 96 GB: see the calculator
Q3_K_M298 GiBMore than 96 GB: see the calculator
UD-Q3_K_XL279 GiBMore than 96 GB: see the calculator
Q3_K_S271 GiBMore than 96 GB: see the calculator
UD-IQ3_XXS260 GiBMore than 96 GB: see the calculator
UD-Q2_K_XL238 GiBMore than 96 GB: see the calculator
Q2_K_L229 GiBMore than 96 GB: see the calculator
Q2_K229 GiBMore than 96 GB: see the calculator
UD-IQ2_M219 GiBMore than 96 GB: see the calculator
UD-IQ2_XXS210 GiBMore than 96 GB: see the calculator
UD-IQ1_M193 GiBMore than 96 GB: see the calculator
UD-IQ1_S179 GiBMore than 96 GB: see the calculator
UD-TQ1_0159 GiBMore than 96 GB: see the calculator

Read every day from Hugging Face. Split files are added together; vision projectors are left out.

How many GPUs to run DeepSeek-V3.1

Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.

GPUFP8 (native)BF16 / FP168-bit4-bit
GeForce RTX 4090 24 GB— (not supported on this GPU)64 (several servers)32 (several servers)24 (several servers)
GeForce RTX 5090 32 GB— (not supported on this GPU)48 (several servers)24 (several servers)16 (several servers)
RTX PRO 6000 Blackwell 96 GB— (not supported on this GPU)16 (several servers)85
Mac M5 Max 128 GB 96 GB usable— (not supported on this GPU)16 (several servers)85
NVIDIA H100 80 GB16 (several servers)24 (several servers)16 (several servers)6
NVIDIA H200 141 GB616 (several servers)64
NVIDIA B200 (HGX) 180 GB4843

8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size. —: the official FP8 weights do not run there: llama.cpp needs a GGUF version, and vLLM needs a GPU with FP8 support.

Cheapest way to run DeepSeek-V3.1 today

For each GPU: the fewest cards that fit DeepSeek-V3.1 with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-11).

On-demand prices from RunPod, Vast.ai, Verda and Azure, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.

KV cache: how memory grows with context

Architecture: 61 layers · MLA (512 + 64 latent) on 61. Its cache stays compact because MLA stores one compressed latent per token and layer instead of full keys and values. Only 40B parameters are active per token, but all 685B must sit in memory: VRAM depends on the total, speed on the active part.

Context per requestBF16 cacheFP8 cache
8K tokens0.54 GiB0.27 GiB
32K tokens2.14 GiB1.07 GiB
128K tokens8.58 GiB4.29 GiB
160K tokens10.7 GiB5.36 GiB

DeepSeek-V3.1 VRAM FAQ

How much VRAM does DeepSeek-V3.1 need?

In BF16 the weights alone take 1250 GiB (1342 GB). In FP8 that is 627 GiB, and about 378 GiB with 4-bit quantization (Q4_K_M); the official FP8 checkpoint is 627 GiB. Each request then adds KV cache: 0.54 GiB at 8K tokens and 2.14 GiB at 32K (BF16 cache).

Can DeepSeek-V3.1 run on a single RTX 4090 (24 GB)?

No. Even in 4-bit it needs 24 (several servers) RTX 4090s. In 4-bit it needs 6 H100 GPUs.

What is the cheapest way to run DeepSeek-V3.1?

On 2026-10-11, the cheapest on-demand setup is 6 × NVIDIA A100 80GB with 4-bit weights on Vast.ai, at $5.60 per hour (about $4,090.92 per month). Next: 5 × RTX PRO 6000 Blackwell (96 GB) with Q4_K_M weights on Vast.ai, at $7.00 per hour. Sized for one 8K-token request; prices are checked every hour.

How many H100 GPUs does DeepSeek-V3.1 need?

With one request and an 8K-token context: 24 (several servers) in BF16, 16 (several servers) in FP8 and 6 in 4-bit (vLLM, 90% of memory usable). Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.

DeepSeek-V3.1 VRAM badge

For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/deepseek-v3-1.json.

DeepSeek-V3.1 VRAM badge

[![DeepSeek-V3.1 VRAM](https://studiotvai.com/badge/deepseek-v3-1.svg)](https://studiotvai.com/vram-requirements/deepseek-v3-1)

Other models

Same family (large moe): MiniMax M2.7 · DeepSeek R1 · Kimi K2.6 · GLM-5.3 · GLM-5.3-Flash · DeepSeek V4 Flash · DeepSeek V4 Pro · DeepSeek V4.1 Flash · MiMo-V2.6 Pro · MiMo-V2.6 Flash · Kimi K3 · Mistral Large 4 · DeepSeek V4 Flash Vision Exp · Qwen3.8 2.4T-A95B · Ornith 1.5 397B · Atria Dawn Preview · Intern S2 397B · DeepSeek-V3 · Kimi-K2.7-Code · Kimi-K2.5 · Kimi-K2 · GLM-5.2 · GLM-5.1 · GLM-5 · MiniMax-M2.5 · MiniMax-M2 · MiMo-V2.5 · Ornith-1.0 397B

All models: Llama 3.1 8B · Mistral Small 3.2 24B · Qwen3 32B · Llama 3.3 70B · Llama 3.1 405B · Qwen3.5 4B · Qwen3.5 9B · Qwen3.8 27B · Qwen3.6 35B-A3B · Qwen3.5 122B-A10B · Qwen3.8-Flash-Next · LFM2.5 8B-A1B · LFM2 24B-A2B · Gemma 4 12B · Gemma 4 31B · Gemma 4 26B-A4B · gpt-oss 20B · gpt-oss 120B · Llama 4 Scout 17B-16E · Llama 4 Maverick 17B-128E · Kolibri-1 · Nemotron 3 Nano 30B-A3B · Nemotron 3 Super 120B-A12B · MiniMax M2.7 · DeepSeek R1 · Kimi K2.6 · GLM-5.3 · GLM-5.3-Flash · DeepSeek V4 Flash · DeepSeek V4 Pro · DeepSeek V4.1 Flash · MiMo-V2.6 Pro · MiMo-V2.6 Flash · Kimi K3 · Mistral Large 4 · Qwen3-Coder-Next 80B-A3B · Qwen3-Coder 30B-A3B · Qwen3-Coder 480B-A35B · DeepSeek V4 Flash Vision Exp · Qwen3.8 2.4T-A95B · Ornith 1.5 397B · Ornith 1.5 35B-A3B · Ornith 1.5 9B · Atria Dawn Preview · Intern S2 397B · Clef · Clef Flash · D1 3B · Humanizer · JEV 27B VL · Mellum2.1 12B-A2.5B · LightOnOCR-3 4B · Spark-X2.5 4B · Agnes-3.0-Qwen · Youtu-Parsing-Omni · Agnes-3.0-Flash · Mistral 7B-v0.3 · Mistral-Small-3.1 24B · Mistral-Nemo · Ministral 8B · DeepSeek-V3 · DeepSeek-R1-Qwen3 8B · DeepSeek-R1-Distill-Qwen 32B · DeepSeek-R1-Distill-Qwen 14B · DeepSeek-R1-Distill-Qwen 7B · DeepSeek-R1-Distill-Llama 8B · Qwen3 4B · Qwen3 8B · Qwen2.5 7B · Qwen3 14B · Qwen2.5 3B · Qwen3.6 27B · Qwen3.5 27B · Qwen2.5-Coder 7B · Qwen2.5 32B · Qwen2.5-Coder 14B · Qwen3.5 35B-A3B · Qwen3 30B-A3B · Qwen2.5 14B · Qwen2.5-Coder 32B · Kimi-K2.7-Code · Kimi-K2.5 · Kimi-K2 · GLM-5.2 · GLM-5.1 · GLM-5 · MiniMax-M2.5 · MiniMax-M2 · MiMo-V2.5 · Ornith-1.0 397B

Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.