pu-rs.org – Processing Unit Ranking System
The SPECfp for AI accelerators.
FLOPS don’t tell the full story. A chip rated at 1000 TFLOPS means nothing if your softmax kernel only achieves 5% utilization. pu-rs.org measures what matters: actual kernel execution time on real hardware, for the operations that AI workloads actually run.
Why this exists
| What we measure | What others report |
|---|---|
| Softmax latency at (64, 4096) f16 | Peak TFLOPS |
| LayerNorm throughput per watt | Memory bandwidth (theoretical) |
| MatMul efficiency vs roofline | Marketing benchmarks |
| Cost per real GOPS | Cloud $/hour (opaque) |
Scope
We benchmark the kernel primitives that compose every AI model:
| Category | Kernels |
|---|---|
| Activation | Softmax, GELU, SiLU |
| Normalization | LayerNorm, RMSNorm |
| Linear Algebra | GEMM, batched MatMul |
| Attention | Scaled Dot-Product Attention |
| Quantization | VQ-Quantize, INT8 dequant |
| Convolution | Conv1D, dilated Conv1D |
| Reduction | Scatter-add, L1-smooth loss |
Devices covered
| Type | Vendors |
|---|---|
| GPU | NVIDIA (A100, H100, H200, B200), AMD (MI300X), Apple (M2/M4 Max), Cambricon |
| TPU | Google (v5e, v6e Trillium) |
| NPU | Huawei Ascend (910B, 910C), AWS Trainium2, Intel Gaudi 3 |
How it works
- Run standardized benchmark scripts on your hardware
- Submit CSV results via pull request
- CI validates format and sanity checks
- Leaderboard updates automatically with per-kernel rankings
All results tagged with git SHA, driver version, toolchain, and number of runs. Median latency reported. Full methodology.
End-to-end complement
Per-kernel latency is only half the story — a chip can win on softmax and still lose on a real model. The DeepSeek decode page reports end-to-end throughput across five accelerators (Ascend 910B2, TPU v2-8, Apple M2 Max, NVIDIA T4, AWS Trainium1) from the same 13-kernel Rust source emitted through the ascend-rs MLIR backends.
Built with ascend-rs kernel infrastructure. Data updated weekly.