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Nvidia A40 mieten.

Name: Nvidia Nvidia A40
Brand: Nvidia
SKU: gpu-a40

Ampere 48GB VRAM 300W From $0.28/hr

Per hour

$0.28

Per day

$6.72

Per week

$47.04

Per month

$202

Provider spread 2 providers · up to 14% cheaper at the low end

Cheapest · $0.28/hr on TensorDock Median $0.30/hr Most expensive · $0.33/hr on RunPod

Price history

Daily median across providers.

Where to rent it

All providers carrying this GPU.

Provider	$/hr	Range	Offers	30d trend	Updated
TensorDock	$0.28/hr	$0.28/hr–$0.28/hr	1	·	about 23 hours	Launch ↗
RunPod	$0.33/hr	$0.33/hr–$0.33/hr	1	·	about 16 hours	Launch ↗

Buy vs rent

Should you rent or own?

Your usage

Hours per day Electricity rate ($/kWh)

Rent on cloud

Per day

—

Per month

—

at $0.28/hr cheapest provider rate

Own on-prem

Electricity per day

—

300W TDP

Rent breakeven on $5,500 MSRP

— days · — months

AI models that fit See all 53 →

Run these on this GPU.

Llama 4 Scout 17B 16E Instruct Fp8 Lora 1× · fp16
Llama 4 Maverick Instruct (17Bx128E) FP8 1× · fp16
Llama 4 Scout (17Bx16E) 1× · fp16

FAQ

Frequently asked.

What AI models can I run on a Nvidia A40?

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 48GB of VRAM.

What's the VRAM minimum to run a model on the Nvidia A40?

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 A40 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.

AI models

Models that run on this GPU.

GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia A40.

Nvidia A40 mieten.

Daily median across providers.

All providers carrying this GPU.

Should you rent or own?

Run these on this GPU.

Frequently asked.

Models that run on this GPU.

Llama 4 Scout 17B 16E Instruct Fp8 Lora

Llama 4 Maverick Instruct (17Bx128E) FP8

Llama 4 Scout (17Bx16E)

Llama 4 Scout Instruct (17Bx16E)

Qwen 2.5 Coder 32B

DeepSeek R1 Distill Qwen 32B

Qwen 2.5 32B

Qwen 3 32B

LLaVA 34B

Code Llama 34B

DeepSeek Coder 33B

Yi-34B

Qwen: Qwen3.6 35B A3B

Qwen: Qwen3.5-35B-A3B

TheDrummer: Skyfall 36B V2

Holo3 35B A3b

Deepseek Coder 33B Instruct

Qwen3.6 35B A3b Fp8

Llama 3.3 70B

Qwen 2.5 72B

DeepSeek R1 Distill Llama 70B

Llama 3.1 70B

Code Llama 70B

Hermes 3 70B

Nous: Hermes 4 70B

Qwen: Qwen2.5 VL 72B Instruct

Sao10K: Llama 3.1 70B Hanami x1

Sao10K: Llama 3.3 Euryale 70B

Magnum v4 72B

Sao10K: Llama 3.1 Euryale 70B v2.2

Sao10k: Llama 3 Euryale 70B v2.1

Meta: Llama 3 70B Instruct

Meta Llama 3.3 70B Instruct Turbo

Meta Llama 3.1 70B Instruct Turbo

nim/meta/llama-3.1-70b-instruct

nim/nvidia/llama-3.1-nemotron-70b-instruct

Cogito V1 Preview Llama 70B

Cogito V1 Preview Llama 70B Turbo

Qwen 2 (72B)

Qwen2.5 72B

Llama 3.1 70B

Meta Llama 3 70B Instruct Turbo

nim/meta/llama-3.3-70b-instruct

Llama 3.1 Nemotron 70B Instruct HF

meta-llama/Llama-3.3-70B-Instruct

Llama 3.3 70B Instruct FP8 Lora

Qwen2.5 72B Instruct

Qwen2 72B Instruct

Qwen2.5 72B Instruct Turbo

Qwen2-VL (72B) Instruct

Hermes-3-Llama-3.1-70B

L3.1-70B-Euryale-v2.2

Meta-Llama-3.1-70B-Instruct

AI models on this GPU.