Rethinking LLM Inference: Why Developer AI Needs a Different Approach

A technical blog post from Augment Code explaining their approach to optimizing LLM inference for code-focused AI applications. The post details how they achieved superior latency and throughput compared to existing solutions by prioritizing context processing speed over decoding, implementing token-level batching, and various technical optimizations. Key metrics include achieving <300ms time-to-first-token for 10k input tokens with Llama3 70B and maintaining >25% GPU FLOPS utilization. The post covers their technical architecture decisions, optimization process, and production system requirements.

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