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LLM Inference Cost and Performance Analysis

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2023

This post analyzes the inference characteristics of Llama, comparing its cost and latency to GPT-3.5. It details the mathematical underpinnings of transformer inference, including FLOPs and memory bandwidth calculations for both prompt processing and token generation. Key findings indicate that Llama is significantly cheaper for prompt-dominated tasks but more expensive for completion-heavy workloads compared to GPT-3.5. The analysis also touches upon memory requirements for KV cache and model weights, and briefly mentions techniques like quantization and speculative sampling for inference optimization.

2023

Inference characteristics of Llama · Cursor

7/20/2023

This post provides a detailed technical breakdown of Llama's inference characteristics. It derives formulas for FLOPs and memory bandwidth per token, distinguishing between prompt processing and token generation. It quantifies the memory requirements for model weights and KV cache, and analyzes the trade-offs between prompt-dominated and completion-heavy workloads when comparing Llama to GPT-3.5. The post also includes a primer on transformer math and discusses factors influencing inference costs.