A100 40GB from $0.09 per GPU-hour
The original 40 GB A100. Half the memory of its successor, which matters for 70B-class models, but the same generation of tensor cores and a price that makes it a sensible training card for smaller models.
Billed per started minute. 320 GB of GPU memory on a 8× node. Persistent storage $0.08 per GB-month. No egress fees.
- GPU memory
- 40GBHBM2 · MIG capable
- Bandwidth
- 1,555GB/sSets token throughput on inference
- Node sizes
- 1× · 2× · 4× · 8×Same price per GPU at every size
- Reclaim rate
- <5%Spot instances reclaimed over 30 days
What you get with each A100 40GB node.
| Node | GPU memory | vCPU | RAM | Local NVMe | Spot | On-demand | Action |
|---|---|---|---|---|---|---|---|
| 1× A100 40GB | 40 GB | 12 | 96 GB | 1 TB | $0.09/h | $0.17/h | Configure |
| 2× A100 40GB | 80 GB | 24 | 192 GB | 2 TB | $0.18/h | $0.34/h | Configure |
| 4× A100 40GB | 160 GB | 48 | 384 GB | 4 TB | $0.36/h | $0.68/h | Configure |
| 8× A100 40GB | 320 GB | 96 | 768 GB | 8 TB | $0.72/h | $1.36/h | Configure |
GPU-to-GPU link: PCIe Gen4 64 GB/s; NVLink bridge 600 GB/s for 2 GPUs. Local NVMe is scratch space wiped when the instance ends; keep anything you need on a persistent disk.
32 A100 40GB GPUs free right now.
Straight from the capacity pool the console books against. Prices are identical in every region — pick the one closest to your data.
A region with zero free GPUs still accepts reserved capacity requests — we hold hardware for a term rather than sell what is already taken.
What fits in 40 GB — and what it costs per hour.
Weights need about 2 GB per billion parameters at FP16 / BF16, 0.5 GB per billion parameters at INT4. We keep 20% of the memory free for activations, the KV cache and the CUDA context, then take the smallest node that still fits.
| Open model | Parameters | FP16 / BF16node · spot price | INT4node · spot price |
|---|---|---|---|
| Mistral 7BAssistants, RAG, classification | 7.2B | 1× $0.09/h | 1× $0.09/h |
| Llama 3.1 8BAssistants, agents, fine-tuning | 8B | 1× $0.09/h | 1× $0.09/h |
| Gemma 2 27BHigher-quality assistants | 27B | 2× $0.18/h | 1× $0.09/h |
| Qwen2.5 32BCode and reasoning | 32B | 2× $0.18/h | 1× $0.09/h |
| Mixtral 8x7BMixture of experts, high throughput | 46.7B | 4× $0.36/h | 1× $0.09/h |
| Llama 3.3 70BThe common production baseline | 70B | 8× $0.72/h | 2× $0.18/h |
| Qwen2.5 72BMultilingual, long context | 72B | 8× $0.72/h | 2× $0.18/h |
| Mixtral 8x22BMixture of experts, large capacity | 141B | over 8× | 4× $0.36/h |
| Llama 3.1 405BLargest widely used open model | 405B | over 8× | 8× $0.72/h |
Real usage depends on context length, batch size and the serving engine: a long context can add tens of gigabytes of KV cache. Treat the table as the floor, not the ceiling — and if a job is close to the limit, take the next node size or quantise one step further.
What a dollar buys on an A100 40GB.
Ranked 7 of 21 data-center models in this catalogue on cost per gigabyte of GPU memory.
GPU memory you get for $1.00 of spot time, per hour.
Bandwidth decides token throughput far more often than raw FLOPS.
Spot price divided by published FP16 tensor throughput.
One GPU on spot, billed per minute, storage excluded.
Against the rest of the market
24 providers · September 2026- Median on-demand elsewhere
- $1.77Our on-demand is 90% below it
- Cheapest on-demand seen
- $0.47Our on-demand $0.17 is 64% below it
- Cheapest spot seen
- $0.40Our spot $0.09 is 78% below it
- 24 hours on one GPU
- $2.16 on spot$4.08 on-demand · $11.28 at the cheapest rate found elsewhere
Index built from published prices for the same GPU across 137 providers (public price index (getdeploying.com)), September 2026. How the guarantee is enforced.
What people run on an A100 40GB.
Fine-tuning with LoRA and QLoRA
Comfortable for parameter-efficient training on 13B–34B models without sharding.
Inference servers
Holds 30B-class models quantised, or several smaller models side by side.
Partitioned serving (MIG)
The GPU can be split into isolated instances, each with its own memory and compute slice.
Anything that can checkpoint
On the spot tier this model is 47% below its own on-demand price, with a 2-minute notice before a reclaim.
NVIDIA A100 40GB, as published by NVIDIA.
Taken from the vendor datasheet. Where a figure is not published, the row is absent rather than estimated.
- Vendor
- NVIDIA
- Architecture
- Ampere (2020)
- Segment
- Datacenter
- Memory
- 40 GB HBM2
- Memory bandwidth
- 1,555 GB/s
- FP16 / BF16 tensor
- 312 TFLOPS
- FP32
- 20 TFLOPS
- CUDA cores
- 6,912
- Board power
- 250 W
- Form factor
- PCIe dual-slot
- Interconnect
- PCIe Gen4 64 GB/s; NVLink bridge 600 GB/s for 2 GPUs
- Multi-instance (MIG)
- Supported by the GPU
- 8-bit float (FP8)
- Not supported — use FP16 or integer quantisation
Source: www.nvidia.com · verified against the vendor document
Software that runs on it
8 templatesPre-built environments, pulled on the node before you land on it. Or bring any OCI image from a public or private registry.
On the spot tier
<5% reclaimed · 30 d- 2 minutes of notice on the metadata endpoint, a webhook and the console
- The instance is stopped, not deleted — disk and IP stay attached
- Auto-relaunch on the next free A100 40GB, or switch the same disk to on-demand
- You save $1.92 a day per GPU against on-demand
If the A100 40GB is not the right fit.
Same memory or more, 33% cheaper on spot
A40 48 GB GDDR6 · Ampere $0.06/h spotNext step up in memory: 48 GB instead of 40 GB
A100 SXM 80GB 80 GB HBM2e · Ampere $0.19/h spotSame Ampere generation
A100 PCIe 80GB 80 GB HBM2e · Ampere $0.09/h spotSame Ampere generation
Or put two of them side by side: A100 40GB vs A40 · A100 40GB vs A40 · A100 40GB vs A100
A100 40GB — the questions that come up
How much does an A100 40GB cost per hour?
Spot is $0.09 per GPU-hour and on-demand is $0.17, both billed per minute — every started minute costs the hourly price divided by 60, so $0.0015 on spot. Reserved capacity is $0.09 per GPU-hour on a monthly commitment. Storage and egress are not included in that rate: persistent disks are $0.08 per GB-month and there are no egress fees.
How many GPUs can I put in one instance?
Node sizes are 1×, 2×, 4×, 8× — up to 8 A100 40GB GPUs in a single virtual machine, with CPU, RAM and local NVMe scaled with the GPU count. The price per GPU is identical at every node size.
Can it run a 70B model?
Yes. At FP16, a 70B model needs about 140 GB for the weights, so it fits on 8× A100 40GB ($0.72 per hour on spot) with roughly 20% of the memory left for activations and the KV cache.
Which regions have it?
Available now in US East (Virginia), EU Central (Frankfurt). Prices are identical in every region; pick the one closest to your data.
How often is a spot A100 40GB reclaimed?
Over the trailing 30 days, <5% of spot instances on this model were reclaimed. You get a 2-minute notice on the metadata endpoint, through a webhook and in the console; the instance is then stopped, never deleted, and the persistent disk stays attached.
Anything else about this model? Ask an engineer — the same people run the nodes.
An A100 40GB for $0.09 an hour, running in under 60 seconds.
Pay as you go — no contracts, no minimum commitment. Add credit, launch, stop whenever you want.
Billed per minute from the moment the instance is reachable. Minimum credit $40, no subscription. 32 A100 40GB GPUs available right now.