vLLM: High-Throughput Local LLM Inference in Pixeltable

Pixeltable 0.6.5 introduces native integration with vLLM, enhancing its capacity to run HuggingFace models at high throughput within computed columns while maintaining key features like declarative pipelines, versioning, and per-row error management. This integration specifically targets GPU throughput constraints, allowing for efficient batch processing and large-scale document classification, summarization, or evaluation tasks. The system supports incremental computation, ensuring only new or altered rows are processed, and provides a version history to compare outputs across changes. While Ollama supports local development and quick experiments on modest hardware, and llama.cpp caters to quantized models on CPUs or Apple Silicon, vLLM offers robust batch inference capabilities on GPU clusters. Additionally, vLLM loads and caches models from HuggingFace, facilitating efficient reuse in computed-column evaluations, with flexible options for engine and sampling parameters.

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