* Studio: pin GPU at 95% headroom and warn on silent CPU fallback

Two related runtime-side fixes for #5106 ("model
loaded fully on RAM instead of VRAM"):

1. GPU pin threshold bump 0.90 -> 0.95
-------------------------------------

``_select_gpus`` and the auto-ctx pin loop in ``start_llama_server``
used a ``pool * 0.90`` threshold to decide whether the model fits on
GPU. Models that needed 91-94% of free VRAM were classified as "does
not fit", so Studio set ``gpu_indices = None`` and shipped
``--fit on`` to llama-server without ``-ngl``. The unsloth
llama.cpp fork's ``--fit on`` then ran with its default
``--fit-target 1024`` (1 GiB margin per device, an upstream default
inherited from ggml-org#18679). On a tight fit where compute
buffers + CUDA context push the projected free below the 1 GiB
target, the fork's fit logic shaves layer weights off the GPU --
slow inference for users whose models would have loaded comfortably
with ``-ngl -1``.

The classic reproducer from #5106 (noahterbest's log):

    GGUF size: 20.8 GB, est. KV cache: 0.1 GB, context: 4096,
    GPUs free: [(0, 22805)], selected: None, fit: True

20.8 GiB on a 22.27 GiB free RTX 4090 is 94% utilization. The model
fits (1.4 GiB headroom), but the 0.90 threshold kicks it to fit
mode. Bumping to 0.95 keeps these in the fits-on-GPU branch and
emits ``-ngl -1`` directly. The fork's ``--fit on`` still serves as
the safety net for the genuinely-too-large case.

The auto-ctx fallback also re-checks fit at 4096 before handing off
to ``--fit on``: a 20.8 GiB model with a 131072 native context fails
the auto loop at native ctx, falls back to ``min(4096, ctx)``, but
its weights + 4096 KV pin to the GPU comfortably. Without the
re-check we still emitted ``--fit on``.

``_fit_context_to_vram``'s 0.90 budget for context binary search is
intentionally left tighter than the pin fraction. That routine
chooses the slider value, where over-promising would OOM at runtime.
``_select_gpus`` decides whether to pin at all, where being
conservative pushes layers to CPU.

2. Belt-and-suspenders: warn on silent CPU fallback
---------------------------------------------------

After ``_wait_for_health`` succeeds, scan llama-server's stdout for
``model buffer size`` lines. If Studio detected GPUs and intended
GPU use but only CPU buffers were allocated, log a structured
warning citing #5106. Markers cover CUDA / ROCm / Metal / Vulkan /
OpenCL / SYCL backends. New ``_gpu_offload_active: Optional[bool]``
field surfaces the result for any future API consumer.

This catches runtime-load failures the install-time fix cannot
cover (cudart bundle pairing PR #5322 is the install-side
companion): user overriding ``--fit-target``, uncommon driver +
toolkit configurations, future regressions in the install path.

Tests: 10 new cases in studio/backend/tests/test_llama_cpp_context_fit.py:
* TestTightFitPinsToGPU x3: noahterbest's exact reproducer (auto and
  explicit ctx pins to GPU at 94%); guard against threshold over-
  broadening (genuine overflow still falls back to ``--fit on``).
* TestClassifyGpuOffload x7: CUDA / ROCm / Metal buffer markers
  return True; CPU-only buffer lines return False; absent buffer
  lines or no GPUs detected return None (no warning).

25 context-fit tests pass (15 baseline + 10 new). 511 tests total
across the affected test files. No regressions.

Refs #5106

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…tic CI test (#5376)

* tests/studio: end-to-end Windows GPU detection mock test (#5106)

Locks in the combined fix from #5322 + #5324 with a synthetic
Windows scenario that CI runners without GPUs can execute. The
test packs the real PyPI win_amd64 wheel layouts (cu12 modular and
the new unsuffixed cu13 nvidia/cu13/bin/x86_64 layout) plus the
exact filename set of the upstream b9103 cudart-llama-bin-win-cuda
bundles, then mocks nvidia-smi output and asserts that:

 * Studio's nvidia-smi probe parses the CSV and reports the GPU.
 * After PR #5322 the install_dir/build/bin/Release/ tree contains
   all three cudart bundle DLLs alongside llama-server.exe.
 * After PR #5324 the PATH built by start_llama_server's win32
   branch lists pip nvidia + torch/lib dirs in addition to the
   binary_dir.
 * cudart64_X.dll, cublas64_X.dll, and cublasLt64_X.dll are
   each reachable from at least one PATH entry, with cudart
   specifically reachable from BOTH the install dir and a pip
   nvidia dir (defence in depth).
 * Bare venvs without pip nvidia wheels still work via #5322's
   binary_dir drop; pre-#5322 installs still work via #5324's
   PATH augmentation.
 * A reconstructed pre-PR scenario (cudart absent from binary_dir
   and pip dirs not on PATH) leaves cudart unreachable, confirming
   the test would catch a future regression.

Bonus housekeeping in studio/install_llama_prebuilt.py: drop the
pointless f-prefix on the literal "llama-" in the
windows_cuda_attempts pairing guard (no behaviour change; lint
nit flagged in the post-merge review).

The mocks model real artifact contents I verified empirically:
 * pip download nvidia-cuda-runtime --platform win_amd64
   produces nvidia/cu13/bin/x86_64/cudart64_13.dll.
 * unzip on the b9103 cudart-llama-bin-win-cuda-13.1-x64.zip
   produces exactly cudart64_13.dll + cublas64_13.dll +
   cublasLt64_13.dll, no executables.
 * objdump -p on the b9103 ggml-cuda.dll shows a static PE
   import on cublas64_13.dll (the root cause of #5106 when
   cublas64_13.dll is unreachable).

Refs #5106 #5322 #5324

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* test_5106_windows_gpu_detection_mock: don't shadow real httpx

This file's name sorts before every other file in studio/backend/tests/
(starts with the digit '5'), so pytest collects it first. The previous
``sys.modules.setdefault("httpx", _httpx_stub)`` ran before any other
test imported real httpx, which meant the stub permanently shadowed
the real module for the rest of the collection. Tests that did
``from httpx import HTTPError, Response`` (test_anthropic_messages,
test_browse_folders_route, test_training_*, etc) then failed at
collection with ``ImportError: cannot import name 'HTTPError'``
because the stub did not define those names. The existing
test_llama_cpp_windows_nvidia_path.py did not trigger the same issue
because it sorts after test_a* / test_b* / etc, by which point the
real httpx has already been imported and setdefault is a no-op.

Switch the stub installation to ``importlib.util.find_spec(name) is
None`` so we only fall back to the stub when the real module truly is
not installed. Backend CI installs httpx, structlog, and the
studio/backend/loggers package is reachable via the sys.path
augmentation a few lines above, so on CI all three find_spec calls
succeed and no stubs are installed at all.

Also add HTTPError and Response to the stub module for the offline
case, so anyone running this test outside CI with httpx absent still
gets a stub that satisfies the broader test suite's imports.

Refs #5106

* test_5106 + llama_cpp: extract win32 PATH helper and harden the regression test

Follow-up to PR #5376's review feedback. Three real findings from the
bot reviewers, plus one stale one.

1. (codex P2 line 201, gemini medium line 209) The regression test's
   _build_path_dirs_like_start_llama_server hand-copied the win32
   branch of LlamaCppBackend.start_llama_server, so a future drop or
   reorder of _windows_pip_nvidia_dll_dirs(sys.prefix) in production
   would have passed the test silently.

   Extract a new staticmethod LlamaCppBackend._build_windows_path_dirs
   (binary_dir, prefix, cuda_path). Production start_llama_server now
   calls this helper. The test's wrapper is reduced to a one-line
   delegate that forwards to the staticmethod, so the regression
   asserts against the exact production logic instead of a parallel
   copy of it.

2. (codex P2 line 245) test_nvidia_smi_probe_reports_synthetic_gpu did
   not clear CUDA_VISIBLE_DEVICES. On a shared GPU runner with the
   variable set in the parent shell, _get_gpu_free_memory() filters
   the mocked CSV and returns [] or falls through to the torch
   fallback. Cleared CUDA_VISIBLE_DEVICES and NVIDIA_VISIBLE_DEVICES
   via monkeypatch.delenv(..., raising=False).

3. (codex P2 line 66) _maybe_stub gated on importlib.util.find_spec
   ("loggers"), which returns a spec because studio/backend/loggers/
   is on sys.path. But the actual import chain loads
   loggers/handlers.py which does `from fastapi import Request,
   Response` at module load. In a lightweight env without fastapi
   installed, the stub never lands and `from core.inference.llama_cpp
   import LlamaCppBackend` raises during collection. Switched
   _maybe_stub to a real import attempt under try / except ImportError
   so the stub falls into place when the package is discoverable but
   not importable. CI has fastapi so this is purely a developer-
   machine ergonomics fix.

The fourth comment (codex P1 line 85 "Keep the httpx stub from leaking
across tests") was already addressed by 7437e73, which replaced the
unconditional sys.modules.setdefault with the find_spec-gated
_maybe_stub. No code change needed.

Production behaviour is unchanged: _build_windows_path_dirs returns
exactly the same ordering start_llama_server used inline
([binary_dir, *pip_dirs, cuda_bin?, cuda_bin_x64?]).

Verification (run inside studio/backend):
  pytest tests/test_5106_windows_gpu_detection_mock.py -v
    -> 10 passed
  pytest tests/test_llama_cpp_*.py tests/test_llama_server_args.py
       tests/test_5106_windows_gpu_detection_mock.py -q
    -> 171 passed
  CUDA_VISIBLE_DEVICES=1 pytest tests/test_5106_windows_gpu_detection_mock.py::TestWindowsGpuDetectionAfter5106Fix::test_nvidia_smi_probe_reports_synthetic_gpu
    -> 1 passed

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* Rename Windows GPU detection test to a generic filename and trim comments

- studio/backend/tests/test_5106_windows_gpu_detection_mock.py
  -> studio/backend/tests/test_windows_gpu_detection_mock.py
  The file is the generic regression suite for Windows GPU detection;
  encoding the issue number in the filename is noise.
- Shorten module docstring, helper docstrings, per-test docstrings and
  inline comments in the renamed test file. No behaviour change,
  all 10 cases still pass.
- Shorten the _build_windows_path_dirs docstring in
  studio/backend/core/inference/llama_cpp.py and update the test-path
  reference; trim the win32 call-site comment to one line.

Local verification:
- pytest studio/backend/tests/test_windows_gpu_detection_mock.py -- 10 passed.
- pytest studio/backend/tests/test_llama_cpp_windows_nvidia_path.py
  studio/backend/tests/test_llama_server_args.py
  studio/backend/tests/test_windows_gpu_detection_mock.py -- 110 passed.

* Studio: harden _wait_for_health against transient httpx ReadError

The probe loop in LlamaCppBackend._wait_for_health only caught
ConnectError and TimeoutException. On Windows, when llama-server.exe
accepts the TCP probe and then dies before sending HTTP headers, the
peer process RST closes the socket. httpx maps this to ReadError
("WinError 10054 -- An existing connection was forcibly closed by the
remote host"), which fell through the except clause and bubbled out of
_wait_for_health, the routes/inference.py load_model handler, and back
to /api/inference/load as an opaque 500.

The crash diagnostic Studio actually wants to surface lives on the
self._process.poll() branch at the top of the loop body: "llama-server
exited with code X. Output: ...". We never reached that branch on the
WinError 10054 path because the very first probe blew up.

Expand the except to also swallow ReadError and RemoteProtocolError so
the next 0.5-second iteration runs the poll() branch. Outcomes:
  * Process really died: structured exit-code + last-stdout log line.
  * Single transient probe blip: silently retried; load succeeds.

Adds studio/backend/tests/test_llama_cpp_wait_for_health.py with five
cases covering happy-path 200, transient ReadError + dead process,
RemoteProtocolError + dead process, ConnectError cycling until success,
and dead process before the first probe. The new cases would have
failed against the old except clause -- ReadError / RemoteProtocolError
would have propagated instead of returning False.

Found while triaging the Windows Studio GGUF CI flake on this PR's
5a6ddc3 push: llama-server.exe (b9203 prebuilt) crashed within 2.2 s of
launch on the GPU-less runner, and Studio reported "WinError 10054"
instead of an upstream-tag-attributable exit-code line.

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Co-authored-by: danielhanchen <michaelhan2050@gmail.com>