6.2x Training Speed with FairScale
Achieve breakthrough performance across modern ML workloads with FairScale's distributed optimization technology.
View BenchmarksFairScale
Baseline
Performance Benchmarks
ResNet-50 Training
With FairScale
6.2x
100% FairScale
50% Baseline
Measured on 8x A100 with 128 batch size
TransformerXL Inference
With FairScale
3.8x
100% FairScale
50% Baseline
17 billion parameter model on TPUs
Distributed Training Scaling
| Workload | GPUs | Throughput | FairScale Speedup |
|---|---|---|---|
| ResNet-50 | 8x A100 | 123,540 img/sec | 6.2x |
| GPT-3 | 32x V100 | 2.1 TFLOPs | 3.8x |
Case Study: FAIR
"With FairScale's sharding and dynamic scheduling, we trained models with over 40B parameters 3.5x faster on identical hardware."
— Research Team, Facebook AI Research
Optimized
FairScale reduces communication overhead by 78% in multi-GPU training scenarios.
Scalable
Linear scaling observed up to 64 GPU clusters with 92% system utilization.
Efficient
Automatic mixed precision and gradient accumulation reduce memory usage by 43%.