Fireworks AI has introduced the Fireworks LLM serving stack, featuring FireAttention, which aims to serve open-source models four times faster than existing alternatives by employing quantization techniques such as FP16 and FP8 without significant quality tradeoffs. This initiative focuses on the Mixtral model, the first open-source model trained on trillions of tokens to support the 'mixture of experts' (MoE) framework. The platform demonstrates improved efficiency in serving MoE models, with particular emphasis on optimizing for long prompts and short token generation scenarios. Fireworks AI's FP8 implementation is highlighted for its ability to shrink model size and enhance deployment efficiency, surpassing existing integer quantization methods. The performance analysis reveals that Fireworks AI's FP8 implementation offers a superior trade-off between accuracy and performance compared to other frameworks like vLLM. Fireworks AI invites individuals interested in advancing AI system optimization to join their team as they continue to innovate in the field of foundation model optimization.