Local Inference of LLMs on Long Contexts: A Data Systems Perspective
Large language models can solve complex problems across a variety of subjects, such as mathematics and coding. For such complex analytical tasks, models with a long context perform better. Serving models with long context is currently mostly done on powerful systems with multiple GPUs, which are too expensive for most users. An alternative is local inference, or serving a model on a user's own machine, which removes per-token costs and rate limits, besides keeping sensitive data private and giving users full control over the models they run. The common understanding, however, is that local LLMs need compression (e.g., quantization) to run on a single GPU. In this paper, we set out to stud...
| Authors | Mattias Dürrmeier, Alberto Lerner |
|---|---|
| Primary category | cs.DB |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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