Performance That Speaks for Itself
Industry-leading benchmarks across inference, training, and power efficiency
Benchmark Results
All benchmarks measured using ResNet-50 on ImageNet with batch size 64, FP16 precision. Results independently verified by MLPerf v3.1.
Model | Category | Performance (TOPS) | Power Efficiency (TOPS/W) | Throughput | Latency |
|---|---|---|---|---|---|
NeuralChip C7 | Cloud | 500 | 1.67 | 10K img/s | 0.8ms |
Competitor H100 | GPU | 450 | 1.29 | 8K img/s | 1.0ms |
Competitor A100 | GPU | 312 | 1.04 | 5.5K img/s | 1.5ms |
NeuralChip S3 | Server | 300 | 4 | 6K img/s | 1.2ms |
Competitor TPU v4 | TPU | 275 | 1.57 | 5K img/s | 1.8ms |
NeuralChip V4 | Vision | 100 | 6.67 | 2K img/s | 1.5ms |
NeuralChip X1 | Edge | 50 | 10 | 1K img/s | 2.0ms |
Benchmark Methodology
Test Configuration: All tests conducted on identical hardware configurations with comparable thermal solutions. Power measurements taken at the wall using calibrated power meters.
Software Stack: Latest stable drivers and frameworks (PyTorch 2.1, TensorFlow 2.14, CUDA 12.2) with manufacturer-recommended optimizations enabled.
Validation: Results verified by independent third-party testing following MLPerf submission guidelines. Raw data and reproduction scripts available upon request.