Sequoia Serves Llama2-70B on RTX4090 with Half-Second Latency

The research community has introduced Sequoia, a new speculative decoding framework designed to efficiently serve the Llama2-70B model on a single RTX4090 graphics card with a latency of approximately half a second per token, with no approximation. This framework, which utilizes 16-bit precision while maintaining the original output distribution, marks a significant improvement over previous methods such as DeepSpeed, which required 5.3 seconds per token, and setups involving 8 x A100 GPUs, which achieved 25ms per token but at a much higher cost. Sequoia's ability to scale speculative decoding to very large speculation budgets, adapt to various hardware configurations, and maintain robustness across different decoding setups has been highlighted as more scalable. Its introduction is seen as a breakthrough in making large AI models more accessible and runnable on consumer-grade GPUs, potentially enabling more localized AI applications and enhancing model performance. The dynamic programming algorithm used to construct the speculative tree has been noted for its cleverness.