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Rethinking LLM Inference: Why Developer AI Needs a Different Approach

Dec 01, 2024 - augmentcode.com
Augment, a developer AI company, is focusing on optimizing large language model (LLM) inference to provide full codebase context without compromising on speed. The company has developed an inference stack that prioritizes context processing speed, enabling it to serve requests with 10k input tokens to Llama3 70B with a time to first token (TTFT) of less than 300ms. Augment's batching strategy allows decoding steps to piggyback on the context processing of other requests, maximizing throughput and minimizing latency.

The company has also addressed other requirements of a production inference system, such as request cancellation and deployment sizes. Augment's optimization process has involved the use of CUDA Graphs, FP8, FlashAttention-3, efficient communication, and custom CUDA kernels. Despite achieving a total completion latency of below 220ms, the company believes there is always more to optimize and is planning to discuss additional challenges in future blog posts.

Key takeaways:

  • Augment is focusing on optimizing large language model (LLM) inference for coding, emphasizing the importance of full codebase context and speed.
  • They have developed an inference stack that can serve requests with 10k input tokens to Llama3 70B with a time to first token (TTFT) of less than 300ms, which is 3x faster than existing solutions.
  • Their optimization process includes strategies such as CUDA Graphs, FP8, FlashAttention-3, efficient communication, and custom CUDA kernels.
  • Despite achieving a total completion latency below 220ms, Augment believes there is always more to optimize and is looking to further improve the understanding of large code bases and increase model size.
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