Together AI has released FlashAttention-3 and FlashAttention-4, significant upgrades to their GPU-accelerated attention mechanism for large language models. FlashAttention-3, designed for Hopper GPUs, achieves up to 75% utilization and 1.5-2x speedup over its predecessor by exploiting new hardware features like Tensor Cores and Tensor Memory Accelerator, and supporting FP8 precision. FlashAttention-4, optimized for Blackwell GPUs, further enhances performance by pipelining computations and addressing bottlenecks in transcendental functions and memory traffic, reaching 71% utilization and offering substantial speedups over existing libraries. AI
IMPACT These optimized attention mechanisms promise significantly faster LLM training and inference, enabling longer context windows and more efficient GPU utilization.
RANK_REASON The cluster describes new algorithmic techniques and software releases (FlashAttention-3 and -4) for optimizing attention mechanisms on specific GPU architectures, detailing performance improvements and hardware feature utilization.
- cuDNN
- FlashAttention-4
- NVIDIA B200
- NVIDIA Blackwell GPU
- NVIDIA Hopper H100
- Together AI
- Triton
- Blackwell GPUs
- FlashAttention-3
- FP8
- Hopper GPUs
- LLMs
- Tensor Cores
- Tensor Memory Accelerator
- Transformer
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