datacenter
Alquilar Nvidia A100 SXM.
Ampere
40GB VRAM
From $1.39/hr
Per hour
$1.39
Per day
$33.36
Per week
$233.52
Per month
$1001
Provider spread
6 providers ·
up to 23% cheaper at the low end
Cheapest · $1.39/hr on io.net
Median $1.50/hr
Most expensive · $1.80/hr on Spheron Network
Price history
Daily median across providers.
Loading...
Where to rent it
All providers carrying this GPU.
AI models that fit
See all 53 →
Run these on this GPU.
- nim/nvidia/llama-3.3-nemotron-super-49b-v1 1× · int4
- NVIDIA: Llama 3.3 Nemotron Super 49B V1.5 1× · int4
- DeepSeek R1 Distill Qwen 14B 1× · fp16
Cloud instances
See all 177 →
Hyperscaler bundles.
Pre-configured on 6 clouds — from $1.52/hr total
($1.48/hr per GPU).
FAQ
Frequently asked.
What AI models can I run on a Nvidia A100 SXM?
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 SXM?
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 SXM 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.
Cloud instance options
Pre-configured instances on hyperscalers.
Whole-instance bundles (GPU + vCPU + RAM + disk) on the major clouds. Per-GPU rate often drops as the count rises. View = spec page · Launch = sign up (affiliate).
Cheapest bundle
$1.52/hr
Lowest $/hr per GPU
$1.48/hr
Providers
6
Instance shapes
177
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G | 1× | 14 | 100 GB | 625 GB | $1.52/hr | $1.52/hr | ||
| A100_sxm4_80G_DGX | 1× | 16 | 160 GB | 1000 GB | $1.52/hr | $1.52/hr | ||
| on-demand · A100 SXM | 1× | 24 | 120 GB | — | $1.60/hr | $1.60/hr | ||
| on-demand · A100 SXM | 1× | 24 | 120 GB | — | $1.60/hr | $1.60/hr | ||
| on-demand · A100 SXM | 1× | 24 | 120 GB | — | $1.60/hr | $1.60/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_80G_SXM4 | 1× | 24 | 116 GB | 2125 GB | $1.80/hr | $1.80/hr | ||
| 1x A100_40G_SXM4 | 1× | 22 | 120 GB | 500 GB | $1.81/hr | $1.81/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| A100_sxm4 | 1× | 30 | 200 GB | 512 GB | $2.19/hr | $2.19/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 1x A100_40G_SXM4 | 1× | 30 | 200 GB | 512 GB | $2.39/hr | $2.39/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| 2x A100_80G_SXM4 | 2× | 44 | 240 GB | 500 GB | $4.70/hr | $2.35/hr | ||
| A100_sxm4_80Gx8_2 | 8× | 240 | 1280 GB | 9578 GB | $11.88/hr | $1.48/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| A100_sxm4x8 | 8× | 120 | 940 GB | 13600 GB | $12.32/hr | $1.54/hr | ||
| 8x A100_80G_SXM4 | 8× | 240 | 1280 GB | 9578 GB | $12.96/hr | $1.62/hr | ||
| 8x A100_80G_SXM4 | 8× | 240 | 1280 GB | 9578 GB | $12.96/hr | $1.62/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_40G_SXM4 | 8× | 120 | 940 GB | 13600 GB | $13.44/hr | $1.68/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| 8x A100_80G_SXM4 | 8× | 192 | 960 GB | 16100 GB | $13.52/hr | $1.69/hr | ||
| p4d.24xlarge | 8× | — | — | — | $21.96/hr | $2.74/hr | ||
| p4d.24xlarge | 8× | — | — | — | $21.96/hr | $2.74/hr | ||
| p4d.24xlarge | 8× | — | — | — | $21.96/hr | $2.74/hr | ||
| BM.GPU4.8 | 8× | — | — | — | $24.40/hr | $3.05/hr | ||
| BM.GPU4.8 | 8× | — | — | — | $24.40/hr | $3.05/hr | ||
| BM.GPU4.8 | 8× | — | — | — | $24.40/hr | $3.05/hr | ||
| A100_sxm4_80Gx8 | 8× | 240 | 1800 GB | 20480 GB | $24.55/hr | $3.07/hr | ||
| A100_sxm4_80Gx8 | 8× | 240 | 1800 GB | 20480 GB | $24.55/hr | $3.07/hr | ||
| A100_sxm4_80Gx8 | 8× | 240 | 1800 GB | 20480 GB | $24.55/hr | $3.07/hr | ||
| A100_sxm4_80Gx8 | 8× | 240 | 1800 GB | 20480 GB | $24.55/hr | $3.07/hr | ||
| A100_sxm4_80Gx8 | 8× | 240 | 1800 GB | 20480 GB | $24.55/hr | $3.07/hr | ||
| ND96asr_v4 | 8× | — | — | — | $27.20/hr | $3.40/hr | ||
| ND96asr_v4 | 8× | — | — | — | $27.20/hr | $3.40/hr | ||
| ND96asr_v4 | 8× | — | — | — | $27.20/hr | $3.40/hr | ||
| p4de.24xlarge | 8× | — | — | — | $27.45/hr | $3.43/hr | ||
| p4de.24xlarge | 8× | — | — | — | $27.45/hr | $3.43/hr | ||
| p4de.24xlarge | 8× | — | — | — | $27.45/hr | $3.43/hr | ||
| BM.GPU.A100-v2.8 | 8× | — | — | — | $32.00/hr | $4.00/hr | ||
| BM.GPU.A100-v2.8 | 8× | — | — | — | $32.00/hr | $4.00/hr | ||
| BM.GPU.A100-v2.8 | 8× | — | — | — | $32.00/hr | $4.00/hr | ||
| ND96amsr_A100_v4 | 8× | — | — | — | $32.77/hr | $4.10/hr | ||
| ND96amsr_A100_v4 | 8× | — | — | — | $32.77/hr | $4.10/hr | ||
| ND96amsr_A100_v4 | 8× | — | — | — | $32.77/hr | $4.10/hr |
Looking for the cheapest rate?
Hyperscaler bundles include managed networking + SLAs. Raw per-GPU rental on P2P marketplaces is typically 3–10× cheaper.
See raw rental rates on the Overview tab →
FAQ
Cloud instances — common questions.
What AI models can I run on a Nvidia A100 SXM?
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 SXM?
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 SXM 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
INT4
INT4
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
INT4
INT4
INT4
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia A100 SXM.
nim/nvidia/llama-3.3-nemotron-super-49b-v1
by Nvidia
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
49B
Context
16K tokens
NVIDIA: Llama 3.3 Nemotron Super 49B V1.5
by Nvidia
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
49B
Context
131K tokens
DeepSeek R1 Distill Qwen 14B
by DeepSeek
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
15B
Context
128K tokens
Qwen 2.5 14B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
15B
Context
128K tokens
Qwen 3 14B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
15B
Context
128K tokens
Phi-4
by Microsoft
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
15B
Context
16K tokens
Phi-3 Medium
by Microsoft
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
14B
Context
128K tokens
DeepSeek Coder V2 Lite
by DeepSeek
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
16B (2B active)
Context
128K tokens
Mistral: Ministral 3 14B 2512
by Mistral AI
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
14B
Context
262K tokens
Qwen: Qwen3 14B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
14B
Context
132K tokens
Cogito V1 Preview Qwen 14B
by Deepcogito
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
14B
Context
131K tokens
Deepcoder 14B Preview
by Togethercomputer
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
14B
Context
131K tokens
Qwen2.5 14B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
14B
Context
131K tokens
Qwen3 14B Base
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
14B
Context
33K tokens
Qwen 2.5 14B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
14B
Context
33K tokens
Ministral 3 14B Instruct 2512
by Mistral AI
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
14B
Context
262K tokens
?
INT4
TheDrummer: Skyfall 36B V2
by Thedrummer
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
36B
Context
33K tokens
Qwen3.6 35B A3b Fp8
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
35B
Context
262K tokens
Qwen: Qwen3.5-35B-A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
35B
Context
262K tokens
Qwen: Qwen3.6 35B A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
35B
Context
262K tokens
GLM-5.1
by Zhipu AI
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
203K tokens
?
FP8
ByteDance Seed: Seed-2.0-Lite
by Bytedance Seed
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
262K tokens
Z.ai: GLM 4.7 Flash
by Zhipu AI
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
31B
Context
203K tokens
NVIDIA: Nemotron 3 Nano 30B A3B
by Nvidia
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
262K tokens
AllenAI: Olmo 3 32B Think
by Allen Institute for AI (AI2)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
66K tokens
Qwen: Qwen3 VL 32B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
262K tokens
Qwen: Qwen3 VL 30B A3B Thinking
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
131K tokens
Qwen: Qwen3 VL 30B A3B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
262K tokens
Tongyi DeepResearch 30B A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
131K tokens
Qwen: Qwen3 30B A3B Thinking 2507
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
131K tokens
Baidu: ERNIE 4.5 VL 28B A3B
by Baidu
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
28B
Context
131K tokens
Qwen: Qwen3 Coder 30B A3B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
160K tokens
Qwen: Qwen3 30B A3B Instruct 2507
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
262K tokens
Z.ai: GLM 4 32B
by Zhipu AI
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
128K tokens
Qwen: Qwen3 30B A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
131K tokens
Qwen: Qwen3 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
131K tokens
Qwen2.5 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
131K tokens
Nemotron 3 Nano Omni 30B A3b Reasoning Fp8
by Nvidia
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
131K tokens
Cogito V1 Preview Qwen 32B
by Deepcogito
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
131K tokens
Qwen QwQ-32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
131K tokens
Qwen 2.5 Coder 32B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
16K tokens
Qwen3 30B A3b Base
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
33K tokens
Qwen3 30B A3B Instruct 2507 Lora
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
262K tokens
Nvidia Nemotron 3 Nano 30B A3b Bf16
by Nvidia
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
262K tokens
Qwen2.5 32B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
32B
Context
33K tokens
Gemma 4 31B It Lora
by Google DeepMind
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
31B
Context
262K tokens
Nemotron-3-Nano-Omni-30B-A3B-Reasoning
by Nvidia
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
262K tokens
gemma-4-31B-it-turbo
by Google DeepMind
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
31B
Context
262K tokens
gemma-4-31B-it-Ultra
by Google DeepMind
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
31B
Context
131K tokens
Meta: Muse Glimmer 30B
by Meta AI
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
131K tokens
Nemotron Lightning 3.5 30B A3B
by Nvidia
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
262K tokens
Tencent: Hy-MT2-30B-A3B
by Tencent
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
8K tokens
granite-4.2-30b
by IBM Research
Required GPUs
1× Nvidia A100 SXM
Total VRAM
40 GB
Parameters
30B
Context
131K tokens
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FAQ
AI models on this GPU.
What AI models can I run on a Nvidia A100 SXM?
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 SXM?
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 SXM 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.