双 8GB GPU 机器的实用任务编排（心跳轻量化 + 重任务云端回退）
问题/场景：两张 8GB 显卡希望降低成本但避免本地模型过载。前置条件：已能运行轻量本地模型。实施步骤：把 heartbeat/摘要类任务固定到小模型，重推理与高风险操作走云模型或人工审批，按任务类型做路由。关键命令：`openclaw gateway status`、`openclaw status`。验证：低成本任务稳定执行且无频繁 OOM。风险：模型切换策略配置错误会导致质量/成本波动。
REDDITDiscovered 2026-02-15Author u/Equivalent-Permit893
Prerequisites
- Host has dual 8GB-class GPUs and can run at least one lightweight local model reliably.
- You can classify workloads by latency/cost/risk before routing.
Steps
- Define a task matrix: heartbeat, digest, and simple triage use lightweight local models; coding/research spikes use hosted models.
- Apply conservative concurrency for local runs to avoid VRAM spikes; keep long outputs summarized.
- For side-effect operations (deploy/delete/external send), require human approval regardless of model source.
- Review daily failure/OOM logs and adjust route thresholds instead of blindly upgrading model size.
Commands
openclaw statusopenclaw gateway statusVerify
In a 24h mixed workload window, lightweight tasks complete on local stack without repeated OOM and heavy jobs remain successful via fallback path.
Caveats
- The Reddit thread offers directional guidance only; exact model-fit for dual 8GB cards is hardware/runtime dependent（需验证）.
- Poor routing rules can increase cost by sending too many medium tasks to hosted models.
Source attribution
This tip is aggregated from community/public sources and preserved with attribution.
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