Add cgo implementation for llama.cpp

Run the server.cpp directly inside the Go runtime via cgo
while retaining the LLM Go abstractions.

llm/gpu_cuda.go

@@ -0,0 +1,57 @@
+//go:build linux || windows
+
+package llm
+
+import (
+	"errors"
+	"log"
+
+	"github.com/jmorganca/ollama/api"
+)
+
+/*
+#cgo windows LDFLAGS: -L"/Program Files/NVIDIA Corporation/NVSMI/"
+#cgo linux LDFLAGS: -lnvidia-ml
+
+#include <stdlib.h>
+#include "examples/server/server.h"
+*/
+import "C"
+
+// CheckVRAM returns the free VRAM in bytes on Linux machines with NVIDIA GPUs
+func CheckVRAM() (int64, error) {
+	return int64(C.check_vram()), nil
+}
+
+func NumGPU(numLayer, fileSizeBytes int64, opts api.Options) int {
+	if opts.NumGPU != -1 {
+		return opts.NumGPU
+	}
+	freeBytes, err := CheckVRAM()
+	if err != nil {
+		if !errors.Is(err, errNvidiaSMI) {
+			log.Print(err.Error())
+		}
+		// nvidia driver not installed or no nvidia GPU found
+		return 0
+	}
+
+	/*
+		Calculate bytes per layer, this will roughly be the size of the model file divided by the number of layers.
+		We can store the model weights and the kv cache in vram,
+		to enable kv chache vram storage add two additional layers to the number of layers retrieved from the model file.
+	*/
+	bytesPerLayer := fileSizeBytes / numLayer
+
+	// 75% of the absolute max number of layers we can fit in available VRAM, off-loading too many layers to the GPU can cause OOM errors
+	layers := int(freeBytes/bytesPerLayer) * 3 / 4
+
+	// TODO - not sure on this part... if we can't fit all the layers, just fallback to CPU
+	// if int64(layers) < numLayer {
+	// 	log.Printf("%d MB VRAM available, insufficient to load current model (reuires %d MB) - falling back to CPU %d", freeBytes/(1024*1024), fileSizeBytes/(1024*1024))
+	// 	return 0
+	// }
+	log.Printf("%d MB VRAM available, loading up to %d GPU layers out of %d", freeBytes/(1024*1024), layers, numLayer)
+
+	return layers
+}