Rent Nvidia Tesla V100S.

Volta 32GB VRAM 250W 2019

AI models

Models that run on this GPU.

Open-weights models that fit this card for inference, with the fewest cards and the precision that load each one. Computed daily from the model's size and the card's memory. Showing the 60 largest of 235 models that fit.

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FAQ

AI models on this GPU.

What AI models can I run on a Nvidia Tesla V100S?

The list above shows open-weights models whose memory need fits this card: on one card when the model uses at least a quarter of its 32 GB, or on 2, 4 or 8 of them when it is too large for one. Each row gives the fewest cards and the precision (FP16, FP8 or INT4) that load it.

What's the VRAM minimum to run a model on the Nvidia Tesla V100S?

We count the weights (parameters × bytes per weight: FP16 = 2, FP8 = 1, INT4 = 0.5), add the KV cache for an 8K-token context, and add 15% for the runtime. A model fits when that is within 90% of the cards' memory. A 70B model needs about 164 GB at FP16 and about 43 GB at INT4. A longer context needs more.

How does quantization (FP16 vs FP8 vs INT4) affect what fits?

Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.

Can I fine-tune on the Nvidia Tesla V100S or only do inference?

The rows here are for inference. Full fine-tuning needs several times more memory than inference at the same model size, because gradients, optimizer state and activations all live in memory. LoRA and QLoRA cut that a lot. The fine-tune cost tool sizes a run for you.

How are these GPU counts worked out?

We compute them every day from the model's parameter count and the card's memory, with the formula above. They are estimates, not tests we ran. Tokens per second, where shown, is an estimate from the card's memory bandwidth on one card.

Rent Nvidia Tesla V100S.

Daily median across providers.

All providers carrying this GPU.

Run these on this GPU.

Models that run on this GPU.

GLM-4.7

GLM-4.6

GLM-4.5

Z.ai: GLM 5.3 Flash

Xiaomi: MiMo-V2.5

Xiaomi: MiMo-V2.6-Flash

Xiaomi: MiMo-V2-Flash

DeepSeek: DeepSeek V4 Flash Vision Exp

DeepSeek: DeepSeek V4 Flash 0731

Baidu: ERNIE 4.5 300B A47B

Tencent: Hy3 preview

Tencent: Hy3

DeepSeek: DeepSeek V4 Flash

Thinking Machines: Inkling Small

DeepSeek Coder V2 236B

Qwen: Qwen3 235B A22B Instruct 2507

Qwen 3 235B

Qwen: Qwen3 VL 235B A22B Thinking

Qwen: Qwen3 Max

Qwen: Qwen3 235B A22B Thinking 2507

Qwen: Qwen3 VL 235B A22B Instruct

MiniMax: MiniMax M2.5

MiniMax: MiniMax M2.7

MiniMax: MiniMax M2.1

MiniMax: MiniMax M2

MiniMax: MiniMax M3.1 Flash

StepFun: Step 3.7 Flash

Seed-1.8

StepFun: Step 3.5 Flash

Mixtral 8x22B

Mistral: Devstral 2 2512

Ling-3.0-flash

Ling 3.0 Flash Fin

Ling-3.0-flash-VL

Mistral Large 2

Qwen: Qwen3.5-122B-A10B

Mistral: Mistral Small 4

NVIDIA: Nemotron 3 Super

GPT-OSS 120B

Poolside: Laguna S 2.1

Llama 4 Scout (17Bx16E)

Meta: Llama 4 Scout

Z.ai: GLM 4.5V

Z.ai: GLM 4.6V

inclusionAI: Ling-2.6-flash

Prime Intellect: INTELLECT-3

GLM-4.5-Air

Command R+

Llama 3.2 90B Vision

Qwen: Qwen3 Next 80B A3B Thinking

Qwen: Qwen3 Next 80B A3B Instruct

Tencent: Hunyuan A13B Instruct

Qwen: Qwen3 Coder Next

Arcee AI: Virtuoso Large

Qwen 2.5 72B

Qwen2 72B Instruct

Qwen 2 (72B)

Qwen: Qwen2.5 VL 72B Instruct

Magnum v4 72B

Qwen2.5 72B

AI models on this GPU.