Resolves two critical issues preventing robust model switching:
1. Scheduler deadlock: Fixed improper loop control flow that prevented
model unloading from triggering after conflict detection. Added proper
multi-GPU conflict detection and unload sequencing.
2. Silent inference failures: Changed critical cudaSetDevice() calls from
graceful error handling back to CUDA_CHECK to prevent models from
appearing to load successfully but failing silently during inference.
Result: Robust Tesla K80 dual-GPU model switching with self-healing
recovery instead of requiring system reboots.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix CUDA_ERROR_INVALID_VALUE from cuMemAddressReserve by aligning max_pool_size to GPU granularity
- Set dynamic max_pool_size based on 90% of actual GPU memory instead of static 32GB
- Add memory availability check before allocation to prevent OOM
- Tested on Tesla K80 dual GPU setup with successful model loading and chat completions
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add runtime check for BF16 support which requires Compute Capability 8.0+.
Tesla K80 and other CC 3.7 GPUs will fallback to FP16/FP32 operations.
This ensures the upstream BF16 optimizations work on newer GPUs while
maintaining compatibility with legacy hardware.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Added support for new gpt-oss model from upstream
- Preserved CUDA Compute Capability 3.7 (Tesla K80) support
- Kept CUDA 11 configuration alongside CUDA 12
- Maintained all documentation specific to ollama37 fork
- Integrated new tool parsing improvements
- Added new backend methods and patches from upstream
* bf16
* tests
* gpt-oss
* enable gptoss for engine
* rough estimate
* convert to mxfp4
* handle safetensors U8
* clamp glu/linear
* update tokenizer
* MXFP4 support
This implements the Open Compute Microscaling (MX) FP4 format
as a tensor type with backend implementations focusing
on mulmat and mulmatid on CPU, CUDA, and Metal.
* Unit tests for MXFP4 support
This exercises various operations and shapes on both CPU and GPU (if detected
on the system)
* cuda graph
* unit test adjustments
* cuda: optimize memory access
Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4
* mac: fix crash on old macos versions
cblas_sgemm is only supported on v13.3 and up, however bf16 is
only supported on v14+ so we were falling back to ggml-blas and
crashing on bf16 tensors. Checking for the function being null
seems to be the simplest way to condittionally avoid registering the
backend.
* server: Minimum context length for gptoss
This model requires a minimum context length of 8192 to function
effectively. Users can set higher values through all normal mechanisms
but lower values will be silently reset.
* ggml: Multiply by numParallel for gptoss sliding window
When computing the graph size estimate, the context size is already
multiplied by numParallel so estimates reflect that. However, since
sliding window models use a smaller, fixed context size, they need
to manually take numParallel into account.
* gpt-oss integration
includes harmony parser and thinking levels, etc.
* fix sync
* fix tests
* fix lint
---------
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Devon Rifkin <drifkin@drifkin.net>
* Enable CUDA Graphs for gemma3n.
Similar to
https://github.com/ggml-org/llama.cpp/pull/14741,
though ollama has a slightly different model graph
than llama.cpp which requires different workaround
checks.
* Remove residual check by reshaping differently in gemma3n model
This should make the heuristics more robust
- Add Gemma3n model support with text generation capabilities
- Add new CUDA mean operations for improved performance
- Add macOS documentation and performance tests
- Update LLAMA patches for ROCm/CUDA compatibility
- Fix various model conversion and processing issues
- Update CI workflows and build configurations
- Add library model tests and Shakespeare test data
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Reporting params.NumGPULayers can be misleading because it is the
requested number of layers, not the actual number that is loaded.
While they are often the same, there are cases where they might mismatch,
such as if the GPU backend is missing.
We don't get valid UUIDs for AMD GPUs on Windows, so the best option
is to use the ordinal IDs. This brings us in line with what we currently
do on the Ollama server - the only exception is AMD GPUs on Linux, which
falls back to using ordinal IDs. The GGML implementation has no fallback
but it doesn't appear to occur for any of the GPUs that we support.
It's also possible that there are collisions between ordinal IDs for
different libraries - however the only places where we use them are
AMD on Windows and Metal on Mac, which can never occur on the same
system.
This is causing segfaults, so disable it. Currently UUIDs are only
used for debugging purposes, although they planned to be used in
additional ways in the future.
Bug #11211
* Re-remove cuda v11
Revert the revert - drop v11 support requiring drivers newer than Feb 23
This reverts commit c6bcdc4223.
* Simplify layout
With only one version of the GPU libraries, we can simplify things down somewhat. (Jetsons still require special handling)
* distinct sbsa variant for linux arm64
This avoids accidentally trying to load the sbsa cuda libraries on
a jetson system which results in crashes.
* temporary prevent rocm+cuda mixed loading
We don't check the return status after computing the graph, which
can silently lead to bad outputs if we try to keep going and future
computation succeeds. This appears to happens in certain cases on
Apple M2 devices.
Fixes#11070
This enables matching up devices and information reported by the backend
with system management libraries such as nvml to get accurate free
memory reporting.
FromFloatSlice and FromIntSlice return an error if the shape doesn't
match the passed data or if memory can't be allocated. Since these
are inputs, the memory being allocated is system memory rather than VRAM.
In many cases, the caller can't really handle the error and panics.
Empty and Zeros directly panic if they can't allocate memory.
This makes things consistent by panicing for the first two cases,
removing a fair amount of error handling code. This is also consistent
with how Go typically handles these situations.
This provides granular information about the backend memory allocations
required by the runner:
- Per backend
- Per layer
- Weights, cache and graph
- Allocation status
This can be used for debugging and validating memory estimates.
GGML has a function to report the allocated size of a backend buffer.
However, this returns 0 if we tried to allocate a buffer and it failed.
For memory management purposes, it's important to know how much we were
trying to allocate. This extends the API to report attempted sizes for
all buffers and whether it succeeeded.
Currently, when the backend is created, the tensors are loaded at the
same time, which is a slow operation. This separates them to be two
steps:
- Create backend, including enumerating tensors and memory allocation
- Loading tensor data
This allows more flexibility in managing model loading.
* Move quantization logic to GGML via new backend
This moves the model aware logic to Go code and calls GGMLs quantization code for model creation.
* Remove "add model quantizations"
This is no longer needed now that quantization is implemented in Go+GGML code directly.
Successfully completing processing with an errgroup cancels the
associated context. However, we also have a goroutine that is checking
for cancelation of the context. As a result, there is a race where
the goroutine can pick up the cancelation and report an error,
replacing the sucessful error message.
To avoid that, this replaces the goroutine with a cancelation check
when we are reading files. This also has the advantage of stopping
all reads relatively quickly on error and also ensuring that there are
no outstanding I/O operations when we return in this case.
The downside is that if a file read blocks forever (for example, over
the network) then cancelation of the context effectively won't be
honored. However, this is also true for other smaller files we read
and the tensors are read in small chunks (128K), so it's consistent
and better on balance overall.
Worst case graph preallocation was disabled by a27462b
"ollamarunner: Temporarily disable worst case graph preallocation"
since it caused crashes with large batches when not using the GPU.
This backports upstream llama.cpp commit f057808
"ggml: Don't assert fail when tensor data changes (#13222)", which
fixes the underlying bug and allows reverting the previous workaround.
When ggml_backend_buffer_free() is called, the device memory
is released but not all backends consistently release the actual
ggml_backend_buffer_t in system RAM, causing a memory leak.
Bug #10040
