Bringing Practical AI Solutions to Long Sequence Generation
The demand for efficient long-sequence inference support has led to the widespread use of large language models (LLMs) like GPT-4, Gemini, and LWM. However, their auto-regressive nature and increasing memory footprint present challenges in serving them efficiently.
TriForce, developed by researchers from Carnegie Mellon University and Meta AI (FAIR), is a hierarchical speculative decoding system designed to enable scalable long sequence generation. It addresses these challenges by utilizing original model weights and dynamic sparse KV cache, allowing for superior cache selection and lossless drafting.
TriForce uses Transformers, FlashAttention, and PyTorch CUDA graphs to maintain full layer sparsity while minimizing kernel launching overhead. It achieves significant speedups and remarkable efficiency on consumer GPUs.
With a speed of 0.108s/token and a 1.9× speedup with large batches, TriForce is a practical AI solution for revolutionizing long-context model serving.
For more information about TriForce, you can check out the paper.
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