Local LLM survey seeks real-world data

This is the kind of grassroots dataset the local-LLM crowd actually needs: less leaderboard theater, more “does it help me ship work on my machine?” But it will only become trustworthy if the responses are consistent enough to compare across wildly different hardware and runtimes.

• –The prompt targets the right signals: model size, quantization, runtime, chip, RAM, tokens/sec, latency, and real context limits are the variables that decide day-to-day usability.
• –Human-validated entries could surface the gap between synthetic benchmarks and the models people actually prefer for writing, coding, and long-context work.
• –The biggest risk is sample bias: enthusiast hardware, Apple Silicon, and power users may dominate the sheet, so the results should be treated as directional rather than universal.
• –If the spreadsheet gets enough entries, it could become a practical companion to benchmarks, especially for people choosing between Ollama, llama.cpp, MLX, LM Studio, and similar stacks.
• –The post is more community infrastructure than product news, but it speaks directly to the local-first AI workflow trend.