LLM Inference Optimization

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The LLM Inference skill provides comprehensive guidance for serving large language models (LLMs) by comparing and configuring top-tier engines like vLLM, llama.cpp, and TGI. It helps users select the ideal deployment strategy based on hardware availability—ranging from high-performance NVIDIA GPUs to resource-constrained CPUs and Apple Silicon. With detailed insights into GGUF quantization, memory requirements, and advanced techniques like PagedAttention and continuous batching, this skill enables developers to maximize throughput and minimize latency in both production and local development environments.

Características Principales

• Cross-engine comparison for vLLM, llama.cpp, TGI, and Ollama
• GGUF quantization guides for memory-efficient local inference
• Hardware-specific optimization for Apple Silicon (Metal) and CUDA
• Step-by-step troubleshooting for memory constraints and performance bottlenecks
• Advanced throughput techniques including PagedAttention and speculative decoding
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Casos de Uso

• Optimizing inference parameters for specific latency and memory requirements
• Running LLMs on local consumer hardware or edge devices with llama.cpp
• Setting up high-throughput production API servers using vLLM