datacenter
租用 Nvidia A100.
Ampere
80GB VRAM
400W
From $0.48/hr
Per hour
$0.48
Per day
$11.52
Per week
$80.64
Per month
$346
Provider spread
11 providers ·
up to 77% cheaper at the low end
Cheapest · $0.48/hr on TensorDock
Median $1.10/hr
Most expensive · $2.10/hr on Oblivus
Price history
Daily median across providers.
Loading...
Where to rent it
All providers carrying this GPU.
Buy vs rent
Should you rent or own?
Your usage
Rent on cloud
Per day
—
Per month
—
at $0.48/hr cheapest provider rate
Own on-prem
Electricity per day
—
400W TDP
Rent breakeven on $11,000 MSRP
— days
· — months
Workloads
Suitable workloads.
AI models that fit
See all 57 →
Run these on this GPU.
- Llama 3.3 70B 2× · fp16
- Mixtral 8x22B 4× · fp16
- Qwen 2.5 72B 2× · fp16
Cloud instances
See all 9 →
Hyperscaler bundles.
Pre-configured on 9 clouds — from $0.78/hr total
($0.78/hr per GPU).
FAQ
Frequently asked.
What's a cloud instance bundle for the Nvidia A100?
A pre-configured VM that pairs the Nvidia A100 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?
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 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
$0.78/hr
Lowest $/hr per GPU
$0.78/hr
Providers
9
Instance shapes
9
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| tnr-a100-on-demand | 1× | — | — | — | $0.78/hr | $0.78/hr | ||
| a100-40gb-pcie | 1× | — | — | — | $0.99/hr | $0.99/hr | ||
| gpu_1x_a100_40gb | 1× | — | — | — | $1.10/hr | $1.10/hr | ||
| nova-a100-on-demand | 1× | — | — | — | $1.79/hr | $1.79/hr | ||
| ace-a100-on-demand | 1× | — | — | — | $1.85/hr | $1.85/hr | ||
|
O
Oblivus
|
vm-a100-40g | 1× | — | — | — | $2.10/hr | $2.10/hr | |
| ND96amsr_A100_v4 | 8× | — | — | — | $25.60/hr | $3.20/hr | ||
| p4d.24xlarge | 8× | — | — | — | $25.60/hr | $3.20/hr | ||
| a2-highgpu-8g | 8× | — | — | — | $29.36/hr | $3.67/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?
A pre-configured VM that pairs the Nvidia A100 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?
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 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
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
INT8
INT8
FP16
FP16
INT8
FP16
FP16
FP16
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia A100.
Llama 3.3 70B
by Meta AI
Required GPUs
2× Nvidia A100
Total VRAM
160 GB
Parameters
70B
Context
128K tokens
Mixtral 8x22B
by Mistral AI
Required GPUs
4× Nvidia A100
Total VRAM
320 GB
Parameters
141B (39B active)
Context
66K tokens
Qwen 2.5 72B
by Alibaba (Qwen Team)
Required GPUs
2× Nvidia A100
Total VRAM
160 GB
Parameters
73B
Context
128K tokens
DeepSeek R1 Distill Llama 70B
by DeepSeek
Required GPUs
2× Nvidia A100
Total VRAM
160 GB
Parameters
70B
Context
128K tokens
DeepSeek R1 Distill Qwen 32B
by DeepSeek
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
33B
Context
128K tokens
Llama 3.1 70B
by Meta AI
Required GPUs
2× Nvidia A100
Total VRAM
160 GB
Parameters
70B
Context
128K tokens
Gemma 2 27B
by Google DeepMind
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
27B
Context
8K tokens
Qwen: Qwen3.6 27B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
27B
Context
262K tokens
Qwen: Qwen3.5-27B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
27B
Context
262K tokens
NVIDIA: Nemotron 3 Nano 30B A3B
by Nvidia
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
262K tokens
AllenAI: Olmo 3 32B Think
by Allen Institute for AI (AI2)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
32B
Context
66K tokens
Qwen: Qwen3 VL 32B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
32B
Context
262K tokens
Qwen: Qwen3 VL 30B A3B Thinking
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
131K tokens
Qwen: Qwen3 VL 30B A3B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
262K tokens
Tongyi DeepResearch 30B A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
131K tokens
Qwen: Qwen3 30B A3B Thinking 2507
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
131K tokens
Baidu: ERNIE 4.5 VL 28B A3B
by Baidu
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
28B
Context
131K tokens
Qwen: Qwen3 Coder 30B A3B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
160K tokens
Qwen: Qwen3 30B A3B Instruct 2507
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
262K tokens
Z.ai: GLM 4 32B
by Zhipu AI
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
32B
Context
128K tokens
Qwen: Qwen3 30B A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
131K tokens
Qwen: Qwen3 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
32B
Context
131K tokens
Qwen2.5 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
32B
Context
131K tokens
Nemotron 3 Nano Omni 30B A3b Reasoning Fp8
by Nvidia
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
131K tokens
Cogito V1 Preview Qwen 32B
by Deepcogito
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
32B
Context
131K tokens
Gemma 3 27B It
by Google DeepMind
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
27B
Context
66K tokens
Qwen QwQ-32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
32B
Context
131K tokens
Qwen 2.5 Coder 32B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
32B
Context
16K tokens
Qwen3 30B A3b Base
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
33K tokens
Gemma 3 27B Pt
by Google DeepMind
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
27B
Qwen3 30B A3B Instruct 2507 Lora
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
262K tokens
Nvidia Nemotron 3 Nano 30B A3b Bf16
by Nvidia
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
262K tokens
Medgemma 27B Text It
by Google DeepMind
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
27B
Context
131K tokens
Qwen2.5 32B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
32B
Context
33K tokens
Gemma 3 27B It Lora
by Google DeepMind
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
27B
Gemma 4 31B It Lora
by Google DeepMind
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
31B
Context
262K tokens
Nemotron-3-Nano-Omni-30B-A3B-Reasoning
by Nvidia
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
30B
Context
262K tokens
gemma-4-31B-it-turbo
by Google DeepMind
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
31B
Context
262K tokens
DeepSeek V3
by DeepSeek
Required GPUs
8× Nvidia A100
Total VRAM
640 GB
Parameters
671B (37B active)
Context
128K tokens
DeepSeek R1
by DeepSeek
Required GPUs
8× Nvidia A100
Total VRAM
640 GB
Parameters
671B (37B active)
Context
128K tokens
FLUX.1 Dev
by Black Forest Labs
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
12B
Stable Diffusion 3.5 Large
by Stability AI
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
8B
Llama 3.1 405B
by Meta AI
Required GPUs
8× Nvidia A100
Total VRAM
640 GB
Parameters
405B
Context
128K tokens
Llama 3.2 11B Vision
by Meta AI
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
11B
Context
128K tokens
Qwen 2.5 Coder 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
33B
Context
128K tokens
Gemma 3 27B
by Google DeepMind
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
27B (27B active)
Context
128K tokens
Mistral Large 2
by Mistral AI
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
123B
Context
128K tokens
Command R+
by Cohere
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
104B
Context
128K tokens
GPT-OSS 120B
by OpenAI
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
120B (5B active)
Context
128K tokens
GLM-4.5-Air
by Zhipu AI
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
106B (12B active)
Context
128K tokens
NVIDIA: Nemotron 3 Super
by Nvidia
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
120B
Context
1M tokens
Qwen: Qwen3.5-122B-A10B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
122B
Context
262K tokens
Nvidia Nemotron 3 Super 120B A12b Fp8
by Nvidia
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
120B
Context
262K tokens
Nvidia Nemotron 3 Super 120B A12b Bf16
by Nvidia
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
120B
Context
262K tokens
Qwen3.5 122B A10b Fp8
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
122B
Context
262K tokens
NVIDIA-Nemotron-3-Super-120B-A12B
by Nvidia
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
120B
Context
262K tokens
gpt-oss-120b-Turbo
by OpenAI
Required GPUs
1× Nvidia A100
Total VRAM
80 GB
Parameters
120B
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?
A pre-configured VM that pairs the Nvidia A100 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?
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 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).