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Tier A/B/C wins for the CPU-only Ollama box (no GPU → optimize TTFT and tokens/sec, not VRAM): - Separate qwen2.5-coder provider for the sandbox `!task` path; chat keeps the general model. Auto-selected when chat is Ollama and a coder build is present, override with --code-model. - OllamaProvider num_ctx default 8192→4096 (8192 was a GPU-mindset default that inflates prefill/TTFT on CPU); expose num_thread; add --num-ctx, --num-thread, --num-predict. token_budget default 3000→2000 to fit. - OllamaProvider.stream() generator over Ollama's stream=True chat endpoint (provider half of token streaming; agent/Rust rendering is a follow-up). - Few-shot request→shell exemplars in SANDBOX_SYSTEM to anchor the small model's fenced-command output. - Matryoshka embedding truncation: OllamaEmbedder truncate_dim=256 (--embed-dim) for faster pure-Python cosine and less RAM; query+stored share the dim. - docs/ai-perf-plan.md records all 8 items with status and the server-side env (OLLAMA_NUM_PARALLEL=1, keep_alive) that must be set where ollama serve runs. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
56 lines
3.1 KiB
Markdown
56 lines
3.1 KiB
Markdown
# AI agent: CPU-only performance & code-quality plan
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Hardware reality: the box serving local models is **CPU-only** (Intel i5-8350U,
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4c/8t, no GPU, 62 GB RAM), Ollama 0.3.9. So we optimize **time-to-first-token**
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(prefill is O(context)) and **tokens/sec**, not VRAM. GPU knobs (flash attention,
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KV-cache quant) are no-ops here.
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## Status
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### Tier A — high impact / low effort
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1. **`qwen2.5-coder` for the sandbox/code path.** *(done)* `qwen2.5-coder:1.5b`
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pulled and wired as a separate code provider used only by `!task`; chat keeps
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the general model. Same speed, better shell/code. Auto-selected when the chat
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provider is Ollama and the coder model is present; override with `--code-model`.
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2. **Lower `num_ctx` to 4096 + expose `num_thread`.** *(done)* OllamaProvider
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default `num_ctx` 8192→4096 (8192 was a GPU-mindset default that inflated TTFT
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on CPU); `token_budget` default 3000→2000 to fit. `--num-ctx`, `--num-thread`,
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`--num-predict` flags added. `num_thread` defaults to Ollama's own (= physical
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cores, 4 here); benchmark 4/6/8.
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3. **Token streaming.** *(partial — provider half done)* `OllamaProvider.stream()`
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now yields deltas from Ollama's `stream=True` chat endpoint. Still TODO (commit 2):
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have the agent emit `_ai:"stream"` delta frames and the Rust client render an
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in-progress bubble. On CPU, perceived latency is TTFT — this will make a slow
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reply feel live.
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4. **Keep model warm + single-flight.** *(partial)* `keep_alive` already 30m
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(prevents mid-session reload). `OLLAMA_NUM_PARALLEL=1` is a **server-side env**
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read by `ollama serve`, not settable from the agent — set it where Ollama is
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launched (documented below).
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### Tier B — code-generation quality
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5. **Few-shot in `SANDBOX_SYSTEM`.** *(done)* 1–2 request→shell exemplars to anchor
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the small model's output format.
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6. **GBNF constrained output.** *(blocked on #7)* Ollama 0.3.9 only supports
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`format: json`, not custom grammars for fenced shell. Needs the upgrade; the
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existing `_extract_commands` parser + few-shot cover the gap meanwhile.
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### Tier C — infra / housekeeping
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7. **Upgrade Ollama 0.3.9 → current.** *(manual, user-run)* System-wide action that
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restarts the daemon other projects share — not run automatically. Buys current
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coder builds, structured-output/grammar support (unblocks #6), bugfixes. CPU
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speed gains are incremental. Suggested: `curl -fsSL https://ollama.com/install.sh | sh`.
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8. **Matryoshka embedding truncation.** *(done)* nomic-embed-text is MRL-trained;
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truncate vectors to 256-dim (`--embed-dim`) for faster pure-Python cosine and
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less RAM. Query + stored use the same dim, so cosine stays correct.
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## Server-side env (set where `ollama serve` runs, e.g. systemd unit or shell)
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```
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OLLAMA_NUM_PARALLEL=1 # single interactive user → all cores to one request
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OLLAMA_KEEP_ALIVE=30m # or -1 to pin forever (62 GB RAM is plenty)
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```
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## Notes
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All grounded in public sources + the Obsidian vault (`research/2026-06-02-*`):
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Q4_K_M is the CPU speed sweet spot, small `num_ctx` beats "context rot", and
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qwen2.5-coder beats the general model at equal size for code.
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