GitLab

Until we get all the memory calculations correct, this can provide
and escape valve for users to workaround out of memory crashes.

* read iogpu.wired_limit_mb on macOS

Fix for https://github.com/ollama/ollama/issues/1826

* improved determination of available vram on macOS

read the recommended maximal vram on macOS via Metal API

* Removed macOS-specific logging

* Remove logging from gpu_darwin.go

* release Core Foundation object

fixes a possible memory leak

The memory changes and multi-variant change had some merge
glitches I missed.  This fixes them so we actually get the cpu llm lib
and best variant for the given system.

This switches darwin to dynamic loading, and refactors the code now that no
static linking of the library is used on any platform

This reduces the built-in linux version to not use any vector extensions
which enables the resulting builds to run under Rosetta on MacOS in
Docker.  Then at runtime it checks for the actual CPU vector
extensions and loads the best CPU library available

* select layers based on estimated model memory usage

* always account for scratch vram

* dont load +1 layers

* better estmation for graph alloc

* Update gpu/gpu_darwin.go
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>

* Update llm/llm.go
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>

* Update llm/llm.go

* add overhead for cuda memory

* Update llm/llm.go
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>

* fix build error on linux

* address comments

---------
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>

This refines the gpu package error handling and fixes a bug with the
system memory lookup on windows.

Refactor where we store build outputs, and support a fully dynamic loading
model on windows so the base executable has no special dependencies thus
doesn't require a special PATH.

This switches the default llama.cpp to be CPU based, and builds the GPU variants
as dynamically loaded libraries which we can select at runtime.

This also bumps the ROCm library to version 6 given 5.7 builds don't work
on the latest ROCm library that just shipped.

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