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Price disclaimer: All prices are indicative list prices in USD, collected automatically from provider APIs and public catalogs and refreshed about every 2 hours. They exclude taxes, storage, egress and other fees unless a provider bundles them, may be out of date, and don't guarantee availability. The provider's own website is authoritative at the time of booking. PetaPrice does not rent out compute and is not a party to any contract with a provider. FLOPS are theoretical peak values from vendor datasheets. More

GPU glossary/Compute

FP4

Also known as: NVFP4, MXFP4

4-bit floating point, used for quantized inference. Only Blackwell and AMD CDNA 4 run it natively.

GPUs with FP4, best value first

Ranked by $ per PFLOP-hour of dense FP4 at each GPU's lowest on-demand price.

1Instinct MI355X288 GB$2.59/h$0.26/PFLOP·h
2RTX 509032 GB$0.44/h$0.26/PFLOP·h
3RTX 5060 Ti16 GB$0.11/h$0.28/PFLOP·h
4B200 SXM180 GB$3.20/h$0.36/PFLOP·h
5RTX 5070 Ti16 GB$0.27/h$0.38/PFLOP·h
6RTX 508016 GB$0.36/h$0.40/PFLOP·h
7B300 SXM288 GB$6.60/h$0.49/PFLOP·h
8RTX PRO 6000 Blackwell96 GB$1.25/h$0.62/PFLOP·h

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Related terms

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.
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.
INT8 and TOPS8-bit integers for quantized inference. Throughput is counted in TOPS (trillions of operations per second) rather than FLOPS.

Compute

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.
$ per PFLOP-hourThe 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.
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.
TF32NVIDIA's 19-bit format that runs FP32 matrix math on tensor cores. PyTorch can use it for FP32 matmuls on Ampere and newer.
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.