ODS
ODS 性能与成本：测量完整本地 AI 一体机
覆盖 bootstrap、模型加载、推理、RAG/媒体、恢复、存储、电力和云回退成本。
你将学会
- Design an end-to-end ODS benchmark
- Allocate local and cloud cost components
- Turn measurements into operational budgets
开始前需要
- Basic Git and command-line usage
- Comfort reading a project README
You can explain the project, run its documented first step, and decide what to verify before adopting it.
先看结论
- Benchmark the full user journey, not only llama-server tokens per second.
- Separate bootstrap, model load, inference, RAG/media, recovery, and operator time.
- Compare local and cloud modes with the same fixture and report cost per accepted outcome.
Choose units that match the user journey
ODS 的性能单位应是用户旅程：首次可聊天、完整模型就绪、首 token、RAG/语音/图像完成、重启恢复和每个被接受结果的成本。固定版本、硬件、模型、上下文、并发、扩展和模式，分别记录冷/热启动、p50/p95、资源、字节、失败与人工运维时间。
Fix the release, hardware tier, model/GGUF, context length, prompt fixture, concurrency, mode, enabled extensions, and storage medium. The README's under-two-minute bootstrap and hardware catalog are documented reference points, not universal throughput or cost guarantees.
Separate cold, warm, and background work
Record cold install, warm restart, model swap, and background download separately. For inference capture p50/p95 latency, tokens per second, queue depth, GPU memory, CPU, and power when available; for RAG include embedding and vector-search time; for voice/image include model load and output bytes.
Bootstrap mode can improve perceived latency while increasing disk and network work in the background. Report both user-visible readiness and total bytes/energy so an optimization does not hide a later contention spike.
Price local ownership and cloud fallback
Local cost includes hardware depreciation, electricity, storage, maintenance, backups, and operator time. Cloud or hybrid mode adds provider token charges, egress, quotas, and privacy review. Keep the mode and provider in every benchmark row; otherwise a cheaper result may simply be using a different service boundary.
A useful score is cost per accepted answer or completed workflow, not cost per raw token. Include failed model loads, retries, moderation or human review, and rollback time in the denominator so integrated-stack trade-offs remain visible.
Publish a decision-ready report
Store the fixture, effective `.env` with secrets removed, model checksum, compose revision, raw timings, resource samples, and a short qualitative review. Compare ODS with Ollama plus Open WebUI or a cloud API only after matching model, prompt, context, and task; the README's comparison table is a scope guide, not a benchmark.
For operations, set budgets for disk growth, GPU memory, request latency, background downloads, and cloud spend. Alert on drift, then canary a model or extension change and retain the previous receipt for rollback.
如何选择
| 比较维度 | 方案 A | 方案 B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
实施步骤
- 1
Pin release, model, hardware tier, mode, extensions, and fixture.
- 2
Measure cold/warm/bootstrap, inference, RAG/media, restart, and swap paths.
- 3
Collect p50/p95, resources, bytes, energy/cost, failures, and review minutes.
- 4
Publish raw evidence, budgets, drift alerts, and a rollback decision.
可复制示例
install/bootstrap -> full model ready -> request p50/p95
RAG/voice/image -> accepted outcome cost -> recovery time常见问题
Does ODS promise a fixed two-minute startup?
No. The README describes bootstrap behavior and measured examples; network, hardware, model, and existing downloads change the result.
Should cloud mode be benchmarked separately?
Yes. Cloud/hybrid changes provider, privacy, quotas, latency, and cost, so label it as a separate mode.
资料来源
- ODS README (captured 2026-08-31)来源核查 2026-08-31
- ODS repository来源核查 2026-08-31