OpenClaw thread maps local model limits

The direct "what does this feel like compared to Haiku or GPT-3.5" mapping is fuzzier than people want. For local agents, latency and context budget matter more than name-brand equivalence, so the real test is whether the model stays snappy enough to keep tool loops moving.

• –Ollama's official memory guidance says 7B models generally need about 8GB, 13B about 16GB, and 70B about 64GB or more, which lines up with the thread's rough ladder.
• –By inference, 32GB is the sweet spot for quantized mid-size models, where 20B-35B class systems start to feel genuinely useful for code and planning.
• –The thread's replies mirror the practical split: 8GB for experimentation, 16GB for entry-level utility, 32GB for usable local agents, 64GB+ for bigger models.
• –OpenClaw-style workflows are especially sensitive to token churn and tool-call latency, so speed and context budget matter more than benchmark bragging rights.
• –The best real-world setup is often hybrid: local models handle privacy-sensitive and routine steps, while cloud models cover the hard reasoning.