56 lines
2.7 KiB
Markdown
56 lines
2.7 KiB
Markdown
# US-042 — GGUF/llama.cpp node backend
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Status: planned
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Priority: High (whole-model GGUF shortcut; distributed path in [ADR-0024](../adr/0024-distributed-gguf-runtime.md))
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Stage: Draft design
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## Context
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The node backend is transformers-only (`model_backend.py` →
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`AutoModelForCausalLM`). For DeepSeek-V4-Flash (158B MoE, official weights FP8
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160 GB) the only quantizations that run on consumer hardware are GGUF
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(IQ2 87 GB → Q4_K_M-XL 175 GB) — llama.cpp format. The transformers-compatible
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quants (FP8, NVFP4, GPTQ W4A16) all need datacenter GPUs. Volunteer machines —
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including our own Strix Halo boxes (128 GB and 80 GB unified memory, GPU via
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Vulkan/ROCm, no FP8 support on RDNA3.5) — run these models today only under
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llama.cpp.
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## Design directions to evaluate (design-it-twice)
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**A. llama.cpp as a per-node shard executor.** Node loads a *layer range* of a
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GGUF via llama-cpp-python; our existing hop protocol (X-Meshnet-Route,
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activations over HTTP/relay) moves hidden states between nodes. Requires
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llama.cpp partial-layer loading and activation import/export — investigate
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feasibility first; this is the riskiest unknown.
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**B. llama.cpp RPC mode under tracker orchestration.** llama.cpp ships a
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native RPC backend that splits one model across machines. The tracker would
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provision/route to an llama.cpp RPC cluster rather than our own hop pipeline.
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Less code, but bypasses our billing/telemetry hop instrumentation and relay
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NAT path — needs a story for both.
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**C. Whole-model GGUF nodes (no sharding).** A node with enough memory serves
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a full GGUF (e.g. IQ2/IQ3 on a 128 GB box); the tracker routes whole requests
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to it (single-hop route). Smallest step, no cross-node activation work, and
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already useful: Strix Halo 128 GB serves DeepSeek-V4-Flash IQ3_XXS (114 GB)
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via llama.cpp Vulkan today.
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Recommended sequencing: C first (small, real value), then A/B investigation.
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## Also in scope
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- Model catalog: allow GGUF entries with quant selection; feature
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`DeepSeek-V4-Flash` IQ4_XS/UD-Q4_K_XL as a curated/featured entry once at
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least direction C works (a featured model nobody can load is an anti-feature)
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- Hardware detection: recognize Strix Halo/unified-memory APUs and Vulkan
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(`hardware.py` currently reports "CPU mode" on these boxes)
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- `MESHNET_DOWNLOAD_DIR`/`--download-dir` applies to GGUF files as well
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## Acceptance criteria (phase C)
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- A node with `--gguf <repo-or-path> --quant IQ3_XXS` serves
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`/v1/chat/completions` via llama.cpp with GPU offload where available
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- Tracker treats it as a full-coverage node (single-hop routes, billing works)
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- Streamed responses work through the tracker proxy and the relay (US-036)
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- `python -m pytest` passes from repo root (llama.cpp behind an optional extra)
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