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Nvidia A100 40GB mieten.

Name: Nvidia Nvidia A100 40GB
Brand: Nvidia
SKU: a10040gb

Ampere 40GB VRAM 400W

AI models

Models that run on this GPU.

GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia A100 40GB.

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FAQ

AI models on this GPU.

What AI models can I run on a Nvidia A100 40GB?

The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 40GB of VRAM.

What's the VRAM minimum to run a model on the Nvidia A100 40GB?

Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.

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 A100 40GB or only do inference?

Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.

Where do these GPU-count recommendations come from?

We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.

Nvidia A100 40GB mieten.

All providers carrying this GPU.

Run these on this GPU.

Frequently asked.

Models that run on this GPU.

DeepSeek R1 Distill Qwen 14B

Qwen 2.5 14B

Qwen 3 14B

Phi-4

Phi-3 Medium

LLaVA 13B

Code Llama 13B

DeepSeek Coder V2 Lite

Baichuan2-13B

Mistral: Ministral 3 14B 2512

Qwen: Qwen3 14B

ReMM SLERP 13B

MythoMax 13B

Cogito V1 Preview Qwen 14B

Deepcoder 14B Preview

Qwen2.5 14B

Qwen3 14B Base

Qwen 2.5 14B Instruct

Ministral 3 14B Instruct 2512

Gemma 3 27B

Gemma 2 27B

Qwen: Qwen3.6 27B

Qwen: Qwen3.5-27B

NVIDIA: Nemotron 3 Nano 30B A3B

AllenAI: Olmo 3 32B Think

Qwen: Qwen3 VL 32B Instruct

Qwen: Qwen3 VL 30B A3B Thinking

Qwen: Qwen3 VL 30B A3B Instruct

Tongyi DeepResearch 30B A3B

Qwen: Qwen3 30B A3B Thinking 2507

Qwen: Qwen3 Coder 30B A3B Instruct

Qwen: Qwen3 30B A3B Instruct 2507

Z.ai: GLM 4 32B

Qwen: Qwen3 30B A3B

Qwen: Qwen3 32B

Baidu: ERNIE 4.5 VL 28B A3B

Qwen2.5 32B

Nemotron 3 Nano Omni 30B A3b Reasoning Fp8

Cogito V1 Preview Qwen 32B

Gemma 3 27B It

Qwen QwQ-32B

Qwen 2.5 Coder 32B Instruct

Qwen3 30B A3b Base

Gemma 3 27B Pt

Qwen3 30B A3B Instruct 2507 Lora

Nvidia Nemotron 3 Nano 30B A3b Bf16

Medgemma 27B Text It

Qwen2.5 32B Instruct

Gemma 3 27B It Lora

Gemma 4 31B It Lora

Nemotron-3-Nano-Omni-30B-A3B-Reasoning

gemma-4-31B-it-turbo

GLM-5.1

ByteDance Seed: Seed-2.0-Lite

AI models on this GPU.