64 lines
2.9 KiB
YAML
64 lines
2.9 KiB
YAML
# Ollama, ROCm backend. Serves models on demand — safe to start before
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# you've put anything in /models.
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#
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# Storage: Ollama's content-addressed blob store is bind-mounted under
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# /models/ollama so all model data on the host lives under /models.
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# Note: Ollama's blobs are SHA256-named, not raw GGUFs — llama.cpp/vLLM
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# can't load them directly. Keep curated GGUFs at /models/<vendor>/...
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# for those engines.
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services:
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ollama:
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image: ollama/ollama:rocm
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container_name: ollama
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restart: unless-stopped
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devices:
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- /dev/kfd:/dev/kfd
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- /dev/dri:/dev/dri
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# Numeric GIDs of host's video (44) and render (991) groups — names
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# don't exist inside the container, but the GIDs need to match the
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# host so /dev/kfd + /dev/dri are accessible.
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group_add:
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- "44"
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- "991"
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environment:
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# Strix Halo's iGPU is gfx1151 (RDNA 3.5), which Ollama's bundled
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# ROCm runtime doesn't recognize — without this override it falls
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# back to CPU silently. 11.0.0 = gfx1100 (Navi 31); the RDNA 3.x
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# ISAs are close enough that gfx1100 kernels run on gfx1151.
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- HSA_OVERRIDE_GFX_VERSION=11.0.0
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# Default context. 256K (the upstream default for Qwen3-Coder)
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# blows the KV cache up to ~25-30 GB and forces ollama to split
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# layers between GPU and CPU. 64K keeps the model fully on GPU
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# while still being plenty for coding contexts.
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- OLLAMA_CONTEXT_LENGTH=65536
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# Perf tuning. Flash attention is the biggest single win on MoE
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# models at long context (20-40 % faster generation). q8_0 KV
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# cache halves KV memory at minor / no quality loss; sometimes
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# faster due to smaller working set. The parallel/loaded-models
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# caps avoid Ollama slicing memory across speculative concurrent
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# requests we never have.
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- OLLAMA_FLASH_ATTENTION=1
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- OLLAMA_KV_CACHE_TYPE=q8_0
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- OLLAMA_NUM_PARALLEL=1
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- OLLAMA_MAX_LOADED_MODELS=1
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# Keep the model resident for 24h instead of the default 5 min.
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# Avoids cold-start latency between sessions; safe because we cap
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# max_loaded_models above so memory doesn't drift.
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- OLLAMA_KEEP_ALIVE=24h
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# Unified-memory recipe. With BIOS UMA=0.5 GB the dedicated VRAM
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# pool is tiny; the model lives in GTT (system RAM the GPU borrows
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# via ttm.pages_limit=33554432 on the kernel cmdline). XNACK +
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# FINE_GRAIN_PCIE put the HIP allocator into demand-paging mode so
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# it treats the merged VRAM+GTT pool as one arena. Same flags as
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# compose/kimi-linear.yml and compose/comfyui.yml — Ollama uses
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# ggml/llama.cpp underneath but its allocator goes through HIP.
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# PYTORCH_HIP_ALLOC_CONF is intentionally absent (Ollama isn't
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# PyTorch).
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- HSA_XNACK=1
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- HSA_FORCE_FINE_GRAIN_PCIE=1
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volumes:
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- /models/ollama:/root/.ollama
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- /models:/models:ro
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ports:
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- "11434:11434"
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