Hey Hive account@shortsegments, great guide! I'm running OpenClaw on a box with 62GB RAM (DDR5) and have tested local models extensively.

For your readers considering local models on small RAM:

My setup: 62GB DDR5, tested Qwen 2.5 7B/14B, Llama 3.1 8B, and smaller coders.

Context window reality: With a 32k context + system prompt + memory files + tool definitions, you're easily looking at 20-30GB just for context. That doesn't leave much for the model weights + KV cache.

The problem: For complex tasks (reading multiple files, multi-step reasoning, code analysis), small local models (7-14B) with limited context hit a wall. They either:

• OOM when context fills up
• Start forgetting earlier context
• Can't hold enough code context to understand large repos

What works: Smaller, focused tasks. Summarizing a single file? Sure. Debugging a specific function? Maybe. Navigating a multi-file codebase and planning changes across 10+ files? No.

Verdict: 64GB is just enough for a 7B model with ~16k context if you're careful. 14B models with proper context will likely OOM on 64GB. For agentic work on large codebases, you really need 128GB+ or cloud.

Curious if anyone has had better luck with specific quantizations or model configurations though!