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This also refines the build process for the ext_server build.

Upstream llama.cpp has added a new dependency with the
NVIDIA CUDA Driver Libraries (libcuda.so) which is part of the
driver distribution, not the general cuda libraries, and is not
available as an archive, so we can not statically link it.  This may
introduce some additional compatibility challenges which we'll
need to keep an eye on.

- prompt cache causes inferance to hang after some time

Make sure we're building an x86 ext_server lib when cross-compiling

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

In some cases we may want multiple variants for a given GPU type or CPU.
This adds logic to have an optional Variant which we can use to select
an optimal library, but also allows us to try multiple variants in case
some fail to load.

This can be useful for scenarios such as ROCm v5 vs v6 incompatibility
or potentially CPU features.

* increase minimum cuda overhead and fix minimum overhead for multi-gpu

* fix multi gpu overhead

* limit overhead to 10% of all gpus

* better wording

* allocate fixed amount before layers

* fixed only includes graph alloc

update submodule to commit `1fc2f265ff9377a37fd2c61eae9cd813a3491bea` until its main branch is fixed

* update submodule to `6efb8eb30e7025b168f3fda3ff83b9b386428ad6`
* unblock condition variable in `update_slots` when closing server

* 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>

If the tree has a stale submodule, make sure we clean it up first

On linux, we link the CPU library in to the Go app and fall back to it
when no GPU match is found. On windows we do not link in the CPU library
so that we can better control our dependencies for the CLI.  This fixes
the logic so we correctly fallback to the dynamic CPU library
on windows.