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afaf7ce8c3
When a model is partially offloaded to system RAM, we can either do the calculations on the CPU or we can temporarily transfer the data to the GPU to do the calculations there. Small batches tend to be better on the CPU, large batches on the GPU. The llamarunner used the GPU in most cases and the ollamarunner used the CPU. Although the ollamarunner saw an improvement in token generation performance, there was a large performance hit in prompt processing (3-10x). There is an existing heuristic to dynamically switch between these two modes but in practice it doesn't have enough information to accurately make that decision. This adds authoritative data to make the check work to get the best of both worlds. Fixes #12037
runner
Note: this is a work in progress
A minimial runner for loading a model and running inference via a http web server.
./runner -model <model binary>
Completion
curl -X POST -H "Content-Type: application/json" -d '{"prompt": "hi"}' http://localhost:8080/completion
Embeddings
curl -X POST -H "Content-Type: application/json" -d '{"prompt": "turn me into an embedding"}' http://localhost:8080/embedding