datacenter
租用 Nvidia A100 SXM.
Ampere
40GB VRAM
From $0.79/hr
Per hour
$0.79
Per day
$18.96
Per week
$132.72
Per month
$569
Provider spread
4 providers ·
up to 60% cheaper at the low end
Cheapest · $0.79/hr on io.net
Median $1.62/hr
Most expensive · $1.99/hr on Lambda Labs
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 8 →
Hyperscaler bundles.
Pre-configured on 2 clouds — from $24.40/hr total
($3.05/hr per GPU).
FAQ
Frequently asked.
What's a cloud instance bundle for the Nvidia A100 SXM?
A pre-configured VM that pairs the Nvidia A100 SXM with a fixed amount of vCPU, RAM, and SSD on a hyperscaler (AWS, Google Cloud, Azure, Oracle, Vultr, etc.). You pay one hourly rate for the whole bundle; the per-GPU rate on the table is just the bundle price divided by the GPU count.
Why is per-GPU pricing on instances different from raw GPU rental rates?
Hyperscaler bundles include managed networking, premium NVMe storage, an SLA, and 24/7 support. P2P marketplaces (Vast.ai, RunPod community, io.net) skip those line items, which is why their raw GPU rates are often 3–10× cheaper. The trade-off is reliability and integration: a hyperscaler instance plugs into your existing VPC, IAM, and observability stack out of the box.
Which hyperscaler is cheapest for the Nvidia A100 SXM?
Sort the table by the $/hr per GPU column — the lowest row is the cheapest hyperscaler today. Pricing shifts as providers re-price spot capacity and refresh quotas, so the leader can change week to week. Click Launch on a row to head to that provider's sign-up page (affiliate link, doesn't change the rate you pay).
Can I run my own image or container on these instances?
Yes. All listed hyperscalers expose standard compute APIs — bring your own AMI/image, mount custom volumes, install your own drivers, run any container runtime you like. The bundle just sets the hardware shape; the OS layer is yours to configure.
How do I get the cheapest rate on the Nvidia A100 SXM overall?
If you can tolerate a P2P marketplace, the Overview tab's "Where to rent it" table usually has the lowest hourly rate by a wide margin. Use a hyperscaler bundle only when you need managed networking, SLAs, or are already inside that cloud's ecosystem (compliance, data-egress costs, IAM).
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
$24.40/hr
Lowest $/hr per GPU
$3.05/hr
Providers
2
Instance shapes
8
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| BM.GPU4.8 | 8× | — | — | — | $24.40/hr | $3.05/hr | ||
| BM.GPU4.8 | 8× | — | — | — | $24.40/hr | $3.05/hr | ||
| ND96asr_v4 | 8× | — | — | — | $27.20/hr | $3.40/hr | ||
| ND96asr_v4 | 8× | — | — | — | $27.20/hr | $3.40/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 |
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's a cloud instance bundle for the Nvidia A100 SXM?
A pre-configured VM that pairs the Nvidia A100 SXM with a fixed amount of vCPU, RAM, and SSD on a hyperscaler (AWS, Google Cloud, Azure, Oracle, Vultr, etc.). You pay one hourly rate for the whole bundle; the per-GPU rate on the table is just the bundle price divided by the GPU count.
Why is per-GPU pricing on instances different from raw GPU rental rates?
Hyperscaler bundles include managed networking, premium NVMe storage, an SLA, and 24/7 support. P2P marketplaces (Vast.ai, RunPod community, io.net) skip those line items, which is why their raw GPU rates are often 3–10× cheaper. The trade-off is reliability and integration: a hyperscaler instance plugs into your existing VPC, IAM, and observability stack out of the box.
Which hyperscaler is cheapest for the Nvidia A100 SXM?
Sort the table by the $/hr per GPU column — the lowest row is the cheapest hyperscaler today. Pricing shifts as providers re-price spot capacity and refresh quotas, so the leader can change week to week. Click Launch on a row to head to that provider's sign-up page (affiliate link, doesn't change the rate you pay).
Can I run my own image or container on these instances?
Yes. All listed hyperscalers expose standard compute APIs — bring your own AMI/image, mount custom volumes, install your own drivers, run any container runtime you like. The bundle just sets the hardware shape; the OS layer is yours to configure.
How do I get the cheapest rate on the Nvidia A100 SXM overall?
If you can tolerate a P2P marketplace, the Overview tab's "Where to rent it" table usually has the lowest hourly rate by a wide margin. Use a hyperscaler bundle only when you need managed networking, SLAs, or are already inside that cloud's ecosystem (compliance, data-egress costs, IAM).
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
Renting for inference?
Pair these models with the cheapest provider on the rental table.
See rental rates on the Overview tab →
FAQ
AI models on this GPU.
What's a cloud instance bundle for the Nvidia A100 SXM?
A pre-configured VM that pairs the Nvidia A100 SXM with a fixed amount of vCPU, RAM, and SSD on a hyperscaler (AWS, Google Cloud, Azure, Oracle, Vultr, etc.). You pay one hourly rate for the whole bundle; the per-GPU rate on the table is just the bundle price divided by the GPU count.
Why is per-GPU pricing on instances different from raw GPU rental rates?
Hyperscaler bundles include managed networking, premium NVMe storage, an SLA, and 24/7 support. P2P marketplaces (Vast.ai, RunPod community, io.net) skip those line items, which is why their raw GPU rates are often 3–10× cheaper. The trade-off is reliability and integration: a hyperscaler instance plugs into your existing VPC, IAM, and observability stack out of the box.
Which hyperscaler is cheapest for the Nvidia A100 SXM?
Sort the table by the $/hr per GPU column — the lowest row is the cheapest hyperscaler today. Pricing shifts as providers re-price spot capacity and refresh quotas, so the leader can change week to week. Click Launch on a row to head to that provider's sign-up page (affiliate link, doesn't change the rate you pay).
Can I run my own image or container on these instances?
Yes. All listed hyperscalers expose standard compute APIs — bring your own AMI/image, mount custom volumes, install your own drivers, run any container runtime you like. The bundle just sets the hardware shape; the OS layer is yours to configure.
How do I get the cheapest rate on the Nvidia A100 SXM overall?
If you can tolerate a P2P marketplace, the Overview tab's "Where to rent it" table usually has the lowest hourly rate by a wide margin. Use a hyperscaler bundle only when you need managed networking, SLAs, or are already inside that cloud's ecosystem (compliance, data-egress costs, IAM).