Repository: ollama/ollama
File: server/sched.go (Go)

Code before:
						*numParallel = p
						return []gpu.GpuInfo{g}
					}
				}
			}
		}

		// TODO future refinements
		// - if multiple Libraries, see if any single GPU in any Library will fit
		// - try subsets of GPUs instead of just falling back to 1 or all in a family

		// Now try all the GPUs
		for _, p := range numParallelToTry {
			req.opts.NumCtx = req.origNumCtx * p
			if ok, estimatedVRAM = llm.PredictServerFit(sgl, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
				slog.Info("new model will fit in available VRAM, loading", "model", req.model.ModelPath, "library", sgl[0].Library, "parallel", p, "required", format.HumanBytes2(estimatedVRAM))
				*numParallel = p
				return sgl
			}
		}
	}
	return nil
}

// If multiple Libraries are detected, pick the Library which loads the most layers for the model
func pickBestPartialByLibrary(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel *int) gpu.GpuInfoList {
	*numParallel = 1
	byLibrary := gpus.ByLibrary()
	if len(byLibrary) <= 1 {
		return gpus
	}
	var bestEstimate uint64
	var bestFit int
	for i, gl := range byLibrary {
		_, estimatedVRAM := llm.PredictServerFit(gl, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts)
		if estimatedVRAM > bestEstimate {
			bestEstimate = estimatedVRAM
			bestFit = i
		}
	}
	return byLibrary[bestFit]
}

// findRunnerToUnload finds a runner to unload to make room for a new model
func (s *Scheduler) findRunnerToUnload() *runnerRef {
	s.loadedMu.Lock()
	runnerList := make([]*runnerRef, 0, len(s.loaded))
	for _, r := range s.loaded {
		runnerList = append(runnerList, r)
	}
	s.loadedMu.Unlock()

Review comment (suggestion):
```suggestion
func pickBestPartialFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel *int) gpu.GpuInfoList {
```

Code after:
						*numParallel = p
						return []gpu.GpuInfo{g}
					}
				}
			}
		}

		// TODO future refinements
		// - if multiple Libraries, see if any single GPU in any Library will fit
		// - try subsets of GPUs instead of just falling back to 1 or all in a family

		// Now try all the GPUs
		for _, p := range numParallelToTry {
			req.opts.NumCtx = req.origNumCtx * p
			if ok, estimatedVRAM = llm.PredictServerFit(sgl, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
				slog.Info("new model will fit in available VRAM, loading", "model", req.model.ModelPath, "library", sgl[0].Library, "parallel", p, "required", format.HumanBytes2(estimatedVRAM))
				*numParallel = p
				return sgl
			}
		}
	}
	return nil
}

// If multiple Libraries are detected, pick the Library which loads the most layers for the model
func pickBestPartialFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel *int) gpu.GpuInfoList {
	*numParallel = 1
	byLibrary := gpus.ByLibrary()
	if len(byLibrary) <= 1 {
		return gpus
	}
	var bestEstimate uint64
	var bestFit int
	for i, gl := range byLibrary {
		_, estimatedVRAM := llm.PredictServerFit(gl, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts)
		if estimatedVRAM > bestEstimate {
			bestEstimate = estimatedVRAM
			bestFit = i
		}
	}
	return byLibrary[bestFit]
}

// findRunnerToUnload finds a runner to unload to make room for a new model
func (s *Scheduler) findRunnerToUnload() *runnerRef {
	s.loadedMu.Lock()
	runnerList := make([]*runnerRef, 0, len(s.loaded))
	for _, r := range s.loaded {
		runnerList = append(runnerList, r)
	}
	s.loadedMu.Unlock()