A100 PCIe 80GB vs L40
Both run on the same nodes, the same templates and the same tiers here — so the choice comes down to memory, bandwidth and price. Every figure below is from the catalogue or the vendor datasheet.
- A100 PCIe
- $0.09/h80 GB · Ampere · 96 available
- L40
- $0.09/h48 GB · Ada Lovelace · 32 available
- Price gap
- NoneOn spot, per GPU-hour
- Over 100 h
- $0.00Difference for the same GPU-hours
What actually separates them.
Four differences that change a decision, computed from the numbers rather than asserted.
Bigger models fit on the A100 PCIe
80 GB against 48 GB. At FP16 that is about 32B parameters on one GPU, against 19B.
Token generation is faster on the A100 PCIe
1,935 GB/s against 864 GB/s. Inference reads the whole model from memory for every token, so bandwidth, not FLOPS, usually sets the ceiling.
More memory per dollar on the A100 PCIe
889 GB per dollar per hour against 533 GB. The usual tie-breaker when both cards fit the job.
Every figure we hold on both cards.
A row is marked only when both values exist and one is genuinely better. A100 PCIe leads on 5 rows, L40 on 3.
| A100 PCIe 80GB | L40 | |
|---|---|---|
| GPU memoryDecides which models fit at all | 80 GB HBM2e | 48 GB GDDR6 |
| Memory bandwidthSets token throughput on inference | 1,935 GB/s | 864 GB/s |
| FP16 tensor | 312 TFLOPS | 181 TFLOPS |
| FP32 | 20 TFLOPS | 91 TFLOPS |
| 8-bit float (FP8)Halves the memory a model needs, where the stack supports it | Not supported | Supported |
| Architecture | Ampere (2021) | Ada Lovelace (2022) |
| Board powerLower is cheaper to run at scale — for us, not for your bill | 300 W | 300 W |
| GPU-to-GPU linkOnly matters for multi-GPU training | PCIe only | PCIe only |
| Node sizes | 1×, 2×, 4×, 8× | 1×, 2×, 4×, 8× |
| Spot price | $0.09/h | $0.09/h |
| On-demand price | $0.19/h | $0.19/h |
| Memory per dollarGPU memory per $1 of spot time, per hour | 889 GB | 533 GB |
| 30-day reclaim rateShare of spot instances reclaimed | <5% | <5% |
| Available now | 96 GPUs | 32 GPUs |
Specifications come from each vendor's datasheet — linked on the A100 PCIe page and the L40 page. Prices are ours, per GPU-hour, billed per minute.
The same models, on one GPU of each.
Weights only, with 20% of the memory kept free for activations and the KV cache. A cross means one GPU is not enough — the model page has the multi-GPU arithmetic.
| Model | Precision | A100 PCIe 80 GB | L40 48 GB |
|---|---|---|---|
| Llama 3.1 8B8B parameters | FP16 · 16 GB | Fits | Fits |
| INT4 · 4 GB | Fits | Fits | |
| Qwen2.5 32B32B parameters | FP16 · 64 GB | Fits | Needs 2× node |
| INT4 · 16 GB | Fits | Fits | |
| Llama 3.3 70B70B parameters | FP16 · 140 GB | Needs 4× node | Needs 4× node |
| INT4 · 35 GB | Fits | Fits | |
| Mixtral 8x22B141B parameters | FP16 · 282 GB | Needs 8× node | Needs 8× node |
| INT4 · 71 GB | Needs 2× node | Needs 2× node |
The same 100 GPU-hours on each.
Compute only, billed per minute. Storage is $0.08 per GB-month on either card.
| A100 PCIe | L40 | |
|---|---|---|
| One hour, spot | $0.09 | $0.09 |
| One day, spot | $2.16 | $2.16 |
| 100 GPU-hours, spot | $9.00 | $9.00 |
| 100 GPU-hours, on-demand | $19.00 | $19.00 |
| A month, reserved | $65.70 | $65.70 |
Take the A100 PCIe when…
- Training and full fine-tuning — Enough memory for optimiser states and activations on models a smaller card can only run in inference.
- Serving large models — A 70B model at FP8 across two GPUs, with room for a long context window.
- Partitioned serving (MIG) — The GPU can be split into isolated instances, each with its own memory and compute slice.
Take the L40 when…
- 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.
- Anything that can checkpoint — On the spot tier this model is 53% below its own on-demand price, with a 2-minute notice before a reclaim.
A100 PCIe or L40?
Which is cheaper, the A100 PCIe or the L40?
The L40, at $0.09 per GPU-hour on spot against $0.09 — 0% less. On-demand: $0.19 against $0.19.
Which one should I take for inference?
Take the one that holds the model with room for the KV cache, then the one with more memory bandwidth. Here that is the A100 PCIe on capacity (80 GB) and the A100 PCIe on bandwidth.
Can I run both on the same job?
Not inside one instance — a node holds GPUs of a single model. You can run two instances in parallel, on the same persistent volume in turn, or split the work: a big card for the model that needs capacity, a cheap one for the parts that do not.
Other comparisons: the full list · all 47 models
Try both for less than the price of deciding.
An hour on each costs $0.18 on spot. Nothing renews, and your disk moves between them.
Billed per minute from the moment the instance is reachable. Minimum credit $40, no subscription.