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[GH-ISSUE #15420] Bug: --ollama-engine runner ignores GGML_CUDA_INIT=1, breaking ROCm bootstrap on gfx1151 APUs #35619
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opened 2026-04-22 20:15:50 -05:00 by GiteaMirror
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Reference: github-starred/ollama#35619
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Originally created by @ftmng on GitHub (Apr 8, 2026).
Original GitHub issue: https://github.com/ollama/ollama/issues/15420
Bug:
--ollama-enginerunner ignoresGGML_CUDA_INIT=1, breaking ROCm bootstrap on gfx1151 APUsSystem Information
/opt/rocm-7.2.1Summary
Ollama 0.20.3 uses a two-step GPU bootstrap:
GGML_CUDA_INIT) → finds GPU ✓GGML_CUDA_INIT=1) → supposed to confirm GPU can init → always fails ✗The verification probe starts the new
--ollama-enginerunner subprocess, which:{"status":2,"progress":0}from/health/dev/kfd— GPU is never initializedfiltering device which didn't fully initializeRoot cause:
GGML_CUDA_INIT=1was an env var for the old llama.cpp/ggml runner. The new--ollama-enginerunner (Go-based) does not implement it. The runner ignores the variable entirely and never initializes the GPU during the bootstrap health check. This means ROCm devices that requireNeedsInitValidation()(all ROCm and CUDA devices perml/device.go:541) are always filtered out on 0.20.x.What Works
rocminfo+rocm-smirecognize the GPU withHSA_OVERRIDE_GFX_VERSION=11.5.1gfx1151kernels confirmed compiled into/usr/local/lib/ollama/rocm/libggml-hip.solddshows no missing deps)ollamauser is inrender+videogroups → has/dev/kfdand/dev/driaccessOLLAMA_VULKAN=1— 4.12 tok/s on qwen2.5:72b-instruct-q5_K_M (100% GPU, 92 GiB available)Detailed Evidence
1. Discovery succeeds, verification fails
From
OLLAMA_DEBUG=1journal output:2. Runner permanently stuck at status:2
Manually starting the runner with the exact same env vars Ollama uses:
Polling health:
Same result without
GGML_CUDA_INIT=1— confirming the variable is completely ignored.lsofconfirms the runner never opens/dev/kfd— the GPU is never touched.3. Vulkan works perfectly
With
OLLAMA_VULKAN=1andVK_ICD_FILENAMES=/usr/share/vulkan/icd.d/radeon_icd.json:4. OLLAMA_NEW_ENGINE=false has no effect
Setting
OLLAMA_NEW_ENGINE=falsein the systemd override does not change behavior — the runner still starts with--ollama-engine.Code Analysis
The issue is in the interaction between two functions in
ml/device.go:And
discover/runner.goline 152:The old llama.cpp runner honored
GGML_CUDA_INIT=1by deeply initializing the GPU during startup. The new--ollama-enginerunner doesn't implement this — it starts in an idle state (status:2) waiting for a model load, regardless ofGGML_CUDA_INIT.Proposed Fix
Option A (minimal, targeted): Skip init validation for gfx115x APUs since the verification mechanism is broken:
Option B (proper fix): Make the
--ollama-enginerunner implementGGML_CUDA_INIT=1by actually initializing the GPU backend during startup and reporting status:1 once successful.Option C (pragmatic): Increase the bootstrap timeout and have the runner respond to
GGML_CUDA_INIT=1with a quick GPU probe that returns status:1 if/dev/kfdis accessible and the ROCm runtime loads.Current Workaround
Vulkan backend via systemd override:
This works (4.12 tok/s on 72B) but native ROCm should be significantly faster based on community reports (~40 tok/s on 30B models with v0.18.0 where the old runner still worked).
Related Issues
--ollama-enginechange)@ftmng commented on GitHub (Apr 8, 2026):
<html><head></head>Update: Source patch confirms two separate bugs
Discovery bug — FIXED with source patch
We patched
NeedsInitValidation()inml/device.goto skip init validation for gfx115x APUs and rebuilt the binary via Docker (golang:1.24image,CGO_ENABLED=1 go build -trimpath -buildmode=pie).The patch:
Result: ROCm is now fully recognized:
No more "filtering device which didn't fully initialize". The discovery bug from the original report is confirmed and the fix works.
Note: we also attempted a binary hex-patch approach before the Docker build. It failed because the Go compiler inlines
NeedsInitValidation()at multiple call sites — we found 10 occurrences of the ROCm 4-byte comparison pattern in the binary, of which 5 matched theNeedsInitValidationsignature. Patching all of them still didn't work, confirming that a proper source rebuild is necessary.Runtime bug — NEW, separate issue
With the patched binary, ROCm discovery succeeds but model loading crashes with SIGSEGV:
The crash occurs inside
llama_model_load_from_fileduring tensor buffer allocation. This happens with both small (llama3.2:1b, 1.2 GiB) and large (qwen2.5:72b, 68 GiB) models. The last log line before the crash is:Tested with Ollama's bundled ROCm libraries (version 7.2.0 based on shared library versions). System ROCm is 7.2.1 but we did not separately test with system libraries replacing the bundled ones — only
OLLAMA_LLM_LIBRARY=rocmwas set, which still uses Ollama's bundledlibggml-hip.so.System memory layout:
Summary
There are two bugs affecting gfx1151 on Ollama 0.20.3:
@rick-github commented on GitHub (Apr 8, 2026):
0.20.3-rocm discovers the GPU fine on my gfx1151, which suggests that while your patch masks the problem, it doesn't fix the root cause.
@ftmng commented on GitHub (Apr 8, 2026):
<html><head></head>Update: Found a working Docker image, still broken on 0.20.3
Working setup
ollama/ollama:0.16.0-rocmin Docker gives full native ROCm acceleration on gfx1151:Note: use numeric GIDs for
--group-addbecauserenderdoesn't exist as a named group inside the container.Performance on qwen2.5:72b-instruct-q5_K_M (68 GB model)
Still broken on 0.20.3
We tested every combination we could think of on 0.20.3 — none of them result in working ROCm inference:
filtering device which didn't fully initialize)NeedsInitValidation()inml/device.goto skip verification for gfx115x, rebuilt via Docker (golang:1.24). Discovery succeeds (library=ROCm total="94.6 GiB"), but model loading crashes with SIGSEGV inlibggml-hip.soduring buffer allocation0.20.3-rocm— same filtering behavior as native0.18.0-rocm— same filtering behavior0.16.0-rocm— ✅ worksThe only workaround on 0.20.3 is
OLLAMA_VULKAN=1, which provides GPU acceleration but with slower prompt processing (41 vs 117 tok/s).What might help fix this for newer versions
The working 0.16.0-rocm image bundles ROCm 6.x libraries (
driver=60342.13) and uses the older runner. Something changed between 0.16.0 and 0.18.0 that broke both the GPU bootstrap verification and the ROCm runtime on gfx1151. Comparing what changed in the discovery/runner code and the bundled ROCm libraries between these versions might help identify the regression.Note on GPU memory
On Strix Halo, the default VRAM allocation may be too small for large models via ROCm. If you see
</html>cudaMalloc failed: out of memory, increase the GPU memory allocation in BIOS/UEFI.@ftmng commented on GitHub (Apr 8, 2026):
Thanks for the reply. We actually did test
ollama/ollama:0.20.3-rocmin Docker with GPU passthrough (--device /dev/kfd --device /dev/dri) and it showed the same filtering behavior on our system:Could you share your full setup?
On our end,
ollama/ollama:0.16.0-rocmin Docker is the only configuration that gives us working ROCm inference.@rick-github commented on GitHub (Apr 8, 2026):
@ftmng commented on GitHub (Apr 8, 2026):
<html><head></head>Thanks for the detailed logs, very helpful!
I see a key difference: your verification probe successfully initializes rocBLAS:
and takes 454ms to complete. On our system, the probe never gets to rocBLAS initialization and takes only ~90ms before being killed.
Comparing our setups:
It looks like the ROCm DKMS driver (
amdgpu-installpackage) provides better gfx1151 support than the stock Ubuntu kernel module — faster GPU initialization that completes within the bootstrap timeout, and no need forHSA_OVERRIDE_GFX_VERSION.For now,
</html>ollama/ollama:0.16.0-rocmin Docker works on our system with full ROCm acceleration (117 tok/s prompt eval, 4.4 tok/s generation on qwen2.5:72b). We'll try installing the ROCm DKMS driver to see if that fixes 0.20.3 as well.@ftmng commented on GitHub (Apr 8, 2026):
<html><head></head>Resolved: amdgpu-dkms driver update fixes everything
First, I want to apologize — this turned out not to be an Ollama bug. And thank you @rick-github for taking the time to share your detailed logs. The comparison between our setups pointed us in the right direction.
The fix
Updating the
amdgpu-dkmskernel module from 6.16.6 to 6.18.4 resolved all issues. Ollama 0.20.3 now works natively with full ROCm acceleration on gfx1151 — no patches, no Vulkan workaround, no Docker, noHSA_OVERRIDE_GFX_VERSIONneeded.After reboot, the bootstrap probe completes successfully and ROCm is recognized:
Performance on qwen2.5:72b-instruct-q5_K_M (68 GB model)
Tip for other Strix Halo / gfx1151 users
If Ollama shows
filtering device which didn't fully initializeand falls back to CPU, check youramdgpukernel module version:If it's older than 6.16.13, update via the AMD repository. The stock Ubuntu kernel modules (even on 25.10) may ship an older version where the GPU doesn't fully initialize during Ollama's bootstrap probe.
</html>@rick-github commented on GitHub (Apr 8, 2026):
Thank you for taking the effort to examine, experiment and find a resolution.