$ per PFLOP-hour

Also known as: $/PFLOP·h, cost per PFLOP

The price of one GPU-hour divided by the GPU's dense peak PFLOPS at a chosen precision. It shows how much compute a dollar buys, so fast expensive GPUs and slow cheap ones can be compared directly. Lower is better.

Best value right now

Ranked by $ per PFLOP-hour of dense FP16 / BF16 at each GPU's lowest on-demand price.

1Instinct MI355X288 GB$2.59/h$1.03/PFLOP·h
2Instinct MI300X192 GB$1.85/h$1.42/PFLOP·h
3B200 SXM180 GB$3.20/h$1.42/PFLOP·h
4RTX A500024 GB$0.16/h$1.44/PFLOP·h
5A100 SXM 40GB40 GB$0.47/h$1.50/PFLOP·h
6V100 SXM2 16GB16 GB$0.19/h$1.52/PFLOP·h
7Instinct MI325X256 GB$2.00/h$1.53/PFLOP·h
8RTX 4070 SUPER12 GB$0.11/h$1.54/PFLOP·h

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Related terms

FLOPS, TFLOPS, PFLOPSFloating-point operations per second: how much arithmetic a chip can do. 1 TFLOPS is a trillion per second, 1 PFLOPS (PetaFLOPS) is a thousand TFLOPS. PetaPrice shows all compute in PFLOPS.
Dense vs. sparse FLOPSVendors often quote tensor throughput with 2:4 structured sparsity, which doubles the headline number. Almost no training or inference uses it, so PetaPrice always shows dense figures.
GPU-hourOne GPU for one hour. An 8-GPU node for 2 hours is 16 GPU-hours. A GPU running around the clock is about 730 GPU-hours a month.

Compute

PrecisionHow many bits each number uses. Fewer bits mean more throughput and less memory, at some cost in accuracy. The same GPU has a different peak for each precision.
FP16 and BF1616-bit formats and the standard for training and running LLMs. BF16 keeps FP32's range with less precision, which makes training more stable; it needs Ampere, CDNA 2 or newer.
FP88-bit floating point for training and inference, with roughly twice the throughput of FP16. Native on Hopper, Ada, Blackwell and AMD CDNA 3 and newer.
FP44-bit floating point, used for quantized inference. Only Blackwell and AMD CDNA 4 run it natively.
TF32NVIDIA's 19-bit format that runs FP32 matrix math on tensor cores. PyTorch can use it for FP32 matmuls on Ampere and newer.
INT8 and TOPS8-bit integers for quantized inference. Throughput is counted in TOPS (trillions of operations per second) rather than FLOPS.
Tensor cores / matrix coresUnits that multiply small matrices in one step, where nearly all of a GPU's AI throughput comes from. NVIDIA calls them tensor cores, AMD matrix cores.
MFU (model FLOPS utilization)The share of peak FLOPS a real workload achieves. Large training runs typically reach 30–60%, so datasheet peaks are for comparing GPUs, not for predicting run time.

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