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.
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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.
GPU
FP8 (native)
BF16 / FP16
8-bit
4-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)
8
5
Mac M5 Max 128 GB 96 GB usable
— (not supported on this GPU)
16 (several servers)
8
5
NVIDIA H100 80 GB
16 (several servers)
24 (several servers)
16 (several servers)
6
NVIDIA H200 141 GB
6
16 (several servers)
6
4
NVIDIA B200 (HGX) 180 GB
4
8
4
3
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 request
BF16 cache
FP8 cache
8K tokens
0.54 GiB
0.27 GiB
32K tokens
2.14 GiB
1.07 GiB
128K tokens
8.58 GiB
4.29 GiB
160K tokens
10.7 GiB
5.36 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.
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.