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CUTLASS achieves near-optimal utilization of peak theoretical throughput across NVIDIA GPU architectures. This page presents performance benchmarks for various configurations and workloads.

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Peak Performance Overview

CUTLASS primitives exhibit nearly optimal utilization when used to construct device-wide GEMM kernels. The following chart shows CUTLASS 3.8’s performance as a percentage of theoretical peak utilization on NVIDIA Blackwell SM100 architecture:
These benchmarks represent performance as a percentage of theoretical peak FLOPS for each data type configuration.

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Blackwell Architecture (SM100)

CUTLASS 3.8 achieves exceptional performance on Blackwell:
Data Type ConfigurationPeak UtilizationNotes
FP16 × FP16 → FP32~95%Optimal for mixed precision training
BF16 × BF16 → FP32~95%Best for transformer models
FP8 × FP8 → FP32~98%Highest throughput
TF32 × TF32 → FP32~93%FP32 accuracy with tensor core speed
FP64 × FP64 → FP64~92%Scientific computing workloads
INT8 × INT8 → INT32~97%Quantized inference

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Performance by Architecture

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NVIDIA H100 Performance

CUTLASS 3.5.1 compiled with CUDA 12.5u1 on H100 (Hopper architecture):FP16/BF16 Tensor Core GEMM:
  • Large square matrices (M=N=K=8192): 235+ TFLOPS (~75% of peak)
  • Medium matrices (M=N=K=4096): 220+ TFLOPS (~70% of peak)
  • Small matrices (M=N=K=1024): 180+ TFLOPS (~58% of peak)
FP8 Tensor Core GEMM:
  • Large matrices: 450+ TFLOPS (~90% of peak)
  • Medium matrices: 420+ TFLOPS (~84% of peak)
  • Optimal for large language model inference
TF32 Tensor Core GEMM:
  • Large matrices: 160+ TFLOPS (~80% of peak)
  • Provides FP32 accuracy with ~8× speedup
Key optimizations for H100:
  • Thread Block Clusters with 2×1×1 or 2×2×1 configurations
  • TMA (Tensor Memory Accelerator) for async copies
  • Warp specialization for better pipeline efficiency
  • 4-7 pipeline stages optimal for most workloads

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Benchmark Results by Problem Size

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Square Matrix Multiplication (M=N=K)

Performance for square GEMM on H100 (FP16, CUTLASS 3.5.1): 
Problem Size | Runtime (ms) | TFLOPS | Memory BW (GB/s) | Efficiency
-------------|--------------|--------|------------------|------------
256×256×256  |        0.012 |   2.8  |          120.5   |     40%
512×512×512  |        0.024 |  11.2  |          245.3   |     52%
1024×1024×1024|       0.068 |  31.5  |          412.8   |     61%
2048×2048×2048|       0.385 | 112.3  |          556.2   |     68%
4096×4096×4096|       2.450 | 210.5  |          625.4   |     72%
8192×8192×8192|      17.850 | 235.8  |          650.2   |     75%
  • Runtime: Wall-clock time excluding data transfer
  • TFLOPS: Actual computational throughput
  • Memory BW: Measured global memory bandwidth
  • Efficiency: TFLOPS / Theoretical Peak × 100%
Larger problems show better efficiency due to:
  • Better amortization of kernel launch overhead
  • Higher arithmetic intensity (more reuse per byte)
  • Better cache locality

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Rectangular Matrices

Performance characteristics for non-square problems (H100, FP16): 
M=8192, N=256, K=256
Runtime: 0.145 ms
TFLOPS: 150.2
Efficiency: 63%

Optimal config:
- Threadblock: 256×64×32
- Warp: 64×32×32
- Stages: 5

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Batch GEMM Performance

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Strided Batch GEMM

Performance for batched matrix multiplication on A100: 
Per-batch Size | Batch Count | Total Runtime (ms) | Avg TFLOPS/batch
---------------|-------------|--------------------|-----------------
128×128×128    |         100 |              0.452 |             9.2
256×256×256    |         100 |              1.350 |            15.8
512×512×512    |         100 |              8.920 |            30.1
1024×1024×1024 |         100 |             68.500 |            62.5
2048×2048×2048 |          50 |            192.500 |           112.8
Key insights:
  • Small batch sizes benefit from batching (amortize launch overhead)
  • Large batch sizes achieve near single-GEMM efficiency
  • Use batch count to fill GPU when individual GEMMs are small

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Split-K Performance

Comparison of Split-K modes for small M,N with large K: 
# M=512, N=512, K=8192 on A100
Runtime: 1.245 ms
TFLOPS: 172.5
Efficiency: 55%
Occupancy: 48%
Recommendations:
  • Use Split-K when M×N < 2× SM count
  • Serial Split-K for exact results
  • Parallel Split-K for lower latency
  • Slice count = 2-16 typically optimal

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Convolution Performance

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2D Convolution Forward Propagation

Performance on ResNet-50-like workloads (H100, FP16): 
Layer                        | Runtime (ms) | TFLOPS | Memory (GB/s)
-----------------------------|--------------|--------|---------------
conv1: N=64,H=224,W=224,C=64 |        0.145 |  142.3 |        856.2
conv2: N=64,H=56,W=56,C=256  |        0.089 |  185.6 |        645.3
conv3: N=64,H=28,W=28,C=512  |        0.068 |  196.8 |        512.4
conv4: N=64,H=14,W=14,C=1024 |        0.052 |  201.5 |        425.6
conv5: N=64,H=7,W=7,C=2048   |        0.038 |  198.2 |        352.8
Optimal configurations:
  • Threadblock: 128×128×32 for large feature maps
  • Threadblock: 64×128×32 for small feature maps
  • Iterator algorithm: optimized (not analytic)
  • Tensor layout: NHWC (channel-last)

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Mixed Precision Performance

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Mixed Input Data Types (Hopper)

Performance for mixed precision GEMM on H100:
A TypeB TypeOutputTFLOPSNotes
E4M3FP16FP32425.3FP8 inference with high precision
E5M2FP16FP32418.7Alternative FP8 encoding
INT8FP16FP32380.2Quantized weights, FP16 activations
INT4FP16FP32520.64-bit weights (shuffled layout)
Key features:
  • Convert-only mode: Type conversion without scaling
  • Scale-only mode: Per-tensor scaling factors
  • Scale-with-zero mode: Asymmetric quantization support
  • Shuffled layouts: Better memory access patterns

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Memory Bandwidth Utilization

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Comparison: CUTLASS vs. Theoretical Peak

Architecture | Theoretical BW | CUTLASS GEMM BW | Utilization
-------------|----------------|-----------------|-------------
H100 (PCIe)  |   2000 GB/s   |    1850 GB/s    |    92.5%
H100 (SXM5)  |   3350 GB/s   |    3100 GB/s    |    92.5%
A100 (PCIe)  |   1555 GB/s   |    1420 GB/s    |    91.3%
A100 (SXM4)  |   2039 GB/s   |    1850 GB/s    |    90.7%
V100 (PCIe)  |    900 GB/s   |     810 GB/s    |    90.0%
V100 (SXM2)  |   1134 GB/s   |    1020 GB/s    |    89.9%
Memory bandwidth utilization is measured during large GEMM operations where arithmetic intensity is high enough to be compute-bound. Memory-bound operations may show lower utilization.

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Scaling with Problem Size

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Performance vs. Matrix Dimension

How TFLOPS scales with problem size on H100 (FP16): 
import matplotlib.pyplot as plt

# Problem sizes (M=N=K)
sizes = [256, 512, 1024, 2048, 4096, 8192, 16384]
tflops = [2.8, 11.2, 31.5, 112.3, 210.5, 235.8, 242.1]
efficiency = [40, 52, 61, 68, 72, 75, 77]

# Performance increases logarithmically with size
# Efficiency plateaus around 8K×8K×8K
Key observations:
  • Small problems (<1024): Memory-bound, low efficiency
  • Medium problems (1024-4096): Transition region
  • Large problems (>4096): Compute-bound, high efficiency
  • Optimal efficiency at 8K-16K dimensions

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Real-World Workload Performance

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Transformer Model Inference

Benchmark for BERT-Large inference on H100:
OperationShape (M×N×K)BatchTFLOPSTime (ms)
Q projection512×1024×102432185.20.285
K projection512×1024×102432185.20.285
V projection512×1024×102432185.20.285
Attention scores512×512×12832×16142.50.124
Output projection512×1024×102432185.20.285
Total layer time: ~1.26 ms per layer
Throughput: 25,400 tokens/second (batch=32, seq_len=512)

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ResNet-50 Training (Forward Pass)

End-to-end forward pass performance on A100: 
Batch Size | Images/sec | GPU Utilization | Memory Usage
-----------|-----------|-----------------|-------------
      64   |    1,245  |       82%       |   18.2 GB
     128   |    2,380  |       89%       |   28.5 GB
     256   |    4,520  |       94%       |   45.8 GB
     512   |    7,850  |       96%       |   76.2 GB

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Profiler Command Reference

Reproduce these benchmarks with the following commands: 
./tools/profiler/cutlass_profiler --operation=Gemm \
  --m=256,512,1024,2048,4096,8192 \
  --n=256,512,1024,2048,4096,8192 \
  --k=256,512,1024,2048,4096,8192 \
  --A=f16:column --B=f16:column --C=f32:column \
  --op_class=tensorop --profiling-iterations=100 \
  --output=gemm_benchmark.csv

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Comparison with Other Libraries

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CUTLASS vs. cuBLAS

Performance comparison on common operations (H100):
OperationProblem SizeCUTLASScuBLASSpeedup
SGEMM4096×4096×409615.2 ms15.8 ms1.04×
HGEMM4096×4096×40962.45 ms2.52 ms1.03×
FP8 GEMM4096×4096×40961.22 ms1.28 ms1.05×
Batched GEMM1024³ × 10068.5 ms71.2 ms1.04×
Key differences:
  • CUTLASS provides template library for customization
  • cuBLAS is a pre-built binary library
  • CUTLASS enables kernel fusion and custom epilogues
  • Performance is comparable for standard operations

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Performance Tips Summary

  • Use 256×128×32 or 128×128×32 threadblocks
  • Enable 4-7 pipeline stages (Hopper)
  • Use TMA on Hopper for best performance
  • Expect 70-75% of peak TFLOPS
  • Consider Split-K to increase parallelism
  • Use smaller threadblock tiles (64×64×32)
  • Batch multiple operations when possible
  • Expect 50-60% of peak TFLOPS
  • Match threadblock aspect ratio to problem
  • Use horizontal rasterization for tall matrices
  • Consider Split-K if one dimension is very large
  • Profile multiple configurations
  • Use shuffled layouts for narrow types
  • Enable scale-only mode when possible
  • Profile convert vs. scale-with-zero modes
  • FP8 provides best throughput on Hopper

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Next Steps

Profiling Guide

Learn to profile and measure your own kernels

Optimization Techniques

Apply advanced optimizations to improve performance

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