Studio: Add Tensor-Parallel llama.cpp support#6040
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This pull request adds support for a Tensor Parallelism toggle (--split-mode tensor) for GGUF models on multi-GPU setups, threading the option from the frontend UI to the backend. The review feedback suggests enhancing robustness by filtering out low-VRAM GPUs (less than the 5 GiB reserve) during tensor-parallel planning to avoid OOM crashes, and refactoring a fragile test that asserts on raw source code strings to use proper mocking instead.
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studio/backend/core/inference/llama_cpp.py (3079-3115)
On multi-GPU setups, attempting to run tensor parallelism when one or more GPUs have less free VRAM than the required compute-graph buffer reserve (_TENSOR_PARALLEL_BUFFER_RESERVE_MIB, i.e., 5 GiB) will result in an immediate out-of-memory (OOM) error or crash on those low-VRAM devices.
We can prevent this by filtering the list of GPUs to only those that have enough free VRAM to accommodate the compute-graph buffer. If fewer than 2 GPUs meet this requirement, we should disable tensor_parallel early and fall back to the standard layer-split planning path. This avoids unnecessary process launches and crashes, providing a much smoother fallback experience.
usable_gpus = []
if tensor_parallel:
reserve_mib = self._TENSOR_PARALLEL_BUFFER_RESERVE_MIB
usable_gpus = [g for g in gpus if g[1] >= reserve_mib]
if len(usable_gpus) < 2:
logger.info(
"Tensor parallelism requested but only %d GPU(s) have "
"enough free VRAM (>= %d MiB) for the compute buffer; "
"ignoring (needs >= 2).",
len(usable_gpus),
reserve_mib,
)
tensor_parallel = False
if tensor_parallel and gpus:
# Tensor-parallel allocation: use all GPUs, weight the split
# by (free - buffer), and cap context to the pooled VRAM
# after weights + per-device compute-graph buffers. See
# _plan_tensor_parallel for the policy + rationale.
target_ctx = (
effective_ctx
if explicit_ctx
else (self._context_length or effective_ctx)
)
(
effective_ctx,
max_available_ctx,
gpu_indices,
tp_tensor_split,
) = self._plan_tensor_parallel(
usable_gpus,
model_size,
target_ctx,
cache_type_kv = cache_type_kv,
n_parallel = n_parallel,
mtp_engaged = _mtp_will_engage,
)
use_fit = Falsestudio/backend/core/inference/llama_cpp.py (2760-2775)
To ensure _plan_tensor_parallel is fully defensive and self-contained, we should filter the input gpus list to only include devices that have enough free VRAM to hold the compute-graph buffer. This prevents planning tensor splits on low-VRAM GPUs that are guaranteed to OOM.
reserve_mib = self._TENSOR_PARALLEL_BUFFER_RESERVE_MIB
usable_gpus = [g for g in gpus if g[1] >= reserve_mib]
gpu_indices = sorted(idx for idx, _ in usable_gpus)
if len(gpu_indices) < 2:
# Tensor parallelism is meaningless on <2 GPUs (the caller drops the
# toggle before this); be defensive and never emit a split here.
return (
target_ctx if target_ctx > 0 else 4096,
target_ctx if target_ctx > 0 else 4096,
gpu_indices,
None,
)
free_by_idx = {idx: free for idx, free in usable_gpus}
pool_mib = sum(free_by_idx.values())
kv_budget_b = (
(pool_mib - len(gpu_indices) * reserve_mib) * 1024 * 1024 - model_size
)studio/backend/tests/test_tensor_parallel.py (408-430)
Asserting on the raw source code string of routes/inference.py using string manipulation (find, rfind) is highly fragile and makes the test suite extremely brittle to minor formatting changes, refactoring, or variable renaming.
Instead, this retry behavior should be tested by mocking llama_backend.load_model to raise an exception on the first call and verify that the route catches it and retries with tensor_parallel=False.
References
- To test a function's
try...exceptblock, mock a dependency called within thetryblock to raise an exception. Do not mock the entire function and set itsside_effectto an exception, as this will not execute the function's own exception handling logic.
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This adds tensor parallelism support for llama.cpp through a new "Tensor Parallelism" checkbox:
When checked, it passes
--split-mode tensortollama-server.On single GPU setups, it gets ignored even if checked.
How
The PR doesn't just pass the flag when the checkbox is checked, it also integrates it with Studio's existing memory allocation framework. Specifically, a new policy was added for the tensor parallel case:
total free VRAM - weights - N*reserve, wherereserveis the per-GPU compute buffer), sized across all GPUs at once (reusing_estimate_kv_cache_bytes).--tensor-splitweighted byfree - reserveper GPU, but only on asymmetric setups where an even split would overflow the smallest GPU.--split-mode tensor), fall back to layer split automatically so the model loads regardless.Each of these changes is necessary to prevent out of memory errors and crashes. They were obtained after extensive testing rather than static code analysis. My asymmetric multi-gpu setup was valuable for developing this PR because it's the hardest type of setup to get tensor parallelism right (it OOMs by default).
About the per-GPU buffer reserve
In tensor mode, llama.cpp allocates a compute-graph buffer on every GPU (the logits buffer,
n_batch x vocab, plus activation scratch). Unlike the KV cache, it can't be derived from the context length: llama.cpp sizes it internally viagraph_reserve, it's roughly equal on each device regardless of the split, and it barely changes with context. In practice it ranged from ~2.3 GB (gemma-3-27B) to ~3.8 GB (gemma-4-31B) on a 256k-vocab model. And--fitis disabled in tensor mode, so nothing else caps the KV cache: underestimate this and the load OOMs.So instead of computing it, the PR reserves a fixed 5 GiB per GPU, above the measured worst case. It's subtracted from each GPU's free VRAM before computing
--tensor-split, and held back per device when capping context. It scales with vocab/batch, not context, so a constant works; the auto-fallback to layer split covers any underestimate.Speed measurements
RTX 6000 Ada (48 GB) + RTX 3090 (24 GB), PCIe, no NVLink, no NCCL. Decode speed in tokens/sec, measured during a 512-token generation after a ~5.9k-token prompt (i.e. at a realistic context depth, not an empty context), layer split (default) vs tensor split. This is a weak interconnect, so NVLink/NCCL setups should see more.
Dense models (tensor parallelism improves decode, more so for larger models):
MoE models (no benefit, can be much slower):
For reference, VRAM splits across both GPUs as expected. For example, gemma-3-27B uses 29.6 GB on a single GPU with layer split, and 16.1 / 15.9 GB split across both with tensor.