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Based on compute capability and driver version, pick
v12 or v11 cuda variants.

This adds new variants for arm64 specific to Jetson platforms

This adjusts linux to follow a similar model to windows with a discrete archive
(zip/tgz) to cary the primary executable, and dependent libraries. Runners are
still carried as payloads inside the main binary

Darwin retain the payload model where the go binary is fully self contained.

* llm: avoid loading model if system memory is too small

* update log

* Instrument swap free space

On linux and windows, expose how much swap space is available
so we can take that into consideration when scheduling models

* use `systemSwapFreeMemory` in check

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Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

This adds logic to detect skew between the driver and
management library which can be attributed to OS overhead
and records that so we can adjust subsequent management
library free VRAM updates and avoid OOM scenarios.

Refine the way we log GPU discovery to improve the non-debug
output, and report more actionable log messages when possible
to help users troubleshoot on their own.

This reverts commit 755b4e4f.

This reverts commit 163cd3e7.

We update the PATH on windows to get the CLI mapped, but this has
an unintended side effect of causing other apps that may use our bundled
DLLs to get terminated when we upgrade.

* gpu: add env var for detecting intel oneapi gpus

* fix build error

This library will give us the most reliable free VRAM reporting on windows
to enable concurrent model scheduling.

Still not complete, needs some refinement to our prediction to understand the
discrete GPUs available space so we can see how many layers fit in each one
since we can't split one layer across multiple GPUs we can't treat free space
as one logical block

Now that we call the GPU discovery routines many times to
update memory, this splits initial discovery from free memory
updating.

This reverts commit 476fb8e8.

* fix oneapi errors on windows 10

Under stress scenarios we're seeing OOMs so this should help stabilize
the allocations under heavy concurrency stress.

This cleans up the logging for GPU discovery a bit, and can
serve as a foundation to report GPU information in a future UX.

Trying to live off the land for cuda libraries was not the right strategy.  We need to use the version we compiled against to ensure things work properly

This moves all the env var reading into one central module
and logs the loaded config once at startup which should
help in troubleshooting user server logs

For some reason this library gives incorrect GPU information, so skip it

We're seeing some corner cases with cudart which might be resolved by
switching to the driver API which comes bundled with the driver package

This change adds support for multiple concurrent requests, as well as
loading multiple models by spawning multiple runners. The default
settings are currently set at 1 concurrent request per model and only 1
loaded model at a time, but these can be adjusted by setting
OLLAMA_NUM_PARALLEL and OLLAMA_MAX_LOADED_MODELS.

Leaving the cudart library loaded kept ~30m of memory
pinned in the GPU in the main process.  This change ensures
we don't hold GPU resources when idle.

count each layer independently when deciding gpu offloading