How to evaluate performance of LLM inference frameworks

The blog post describes how LLM inference frameworks have reached a 'memory wall,' limiting further speed enhancements. It explains misleading metrics, urging developers to understand their system's limits and choose a suitable framework. Practical advice on using optimizations like quantization and sparsity with caution is given, along with the significance of using well-validated models. Lamini's inference engine is designed with these aspects in mind, supporting different GPUs and scenarios while emphasizing caution in memory-intensive LLM operations.

• Transformers are memory bound in the decoding phase.
• Memory wall imposes an upper performance limit.
• Quantization and sparsity should be used cautiously.
• Lamini's engine optimizes for the MLPerf Server scenario.
• Expert teams may aggressively optimize, but it's not recommended for most.