Guides

Benchmark-guided recommendations for local LLMs

Use these guides when you want a direct answer, not a table of numbers. Each guide translates benchmark data into a practical choice for coding, OpenClaw, or consumer hardware.

Guide

Best Local LLM for Apple M5 Max (64GB)

Find the best local LLM for a 64GB Apple M5 Max, with benchmark-backed guidance on why the MLX runtime, MoE models, and unified memory decide your real-world speed and quality.

  • On Apple Silicon the runtime matters more than the model size: MLX mixture-of-experts models reach 67-85 tok/s, while dense GGUF models on Ollama sit at roughly 5-20 tok/s on the same chip.
  • 64GB of unified memory is the real advantage — it lets a 79.7B coding MoE (qwen3-coder-next, ~51GB) run fully on the GPU, which a smaller Mac simply cannot hold.
  • The quality ceiling (~84) comes from the Qwen3.6 27B/35B family; choose based on whether you want raw speed or the very top scores.
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Guide

Best Local LLMs for Coding

Pick a local coding model based on quality, speed, and whether it fits your GPU without constant swapping.

  • Prioritize code quality and instruction following before raw token speed.
  • The ~20B parameter class, led by gpt-oss:20b, dominates the quality charts while retaining lightning-fast speeds.
  • Use the benchmark pages to compare the same model across different hardware and tools.
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Guide

Which Model to Use for OpenClaw

Choose a model for OpenClaw and other agents based on planning quality, robust tool usage, and enough context length to keep the workflow coherent.

  • Robust tool usage and sufficient context length are critical for preventing agent hallucinations.
  • Agent planning and tool selection matter more than tiny speed differences.
  • These guidelines are valid for OpenClaw, Hermes Agent, and other autonomous loops.
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Guide

Consumer Hardware Performance Guide

Understand how VRAM tiers shape local LLM performance on consumer GPUs and why the best card depends on the models you want to run.

  • VRAM capacity usually sets the real ceiling before raw compute does.
  • Each VRAM tier opens or closes different model sizes and quantization choices.
  • The hardware page is the quickest way to compare practical GPU options.
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Guide

Best Local LLM for RX 7900 XTX

Find the best local LLM for RX 7900 XTX with benchmark-backed guidance on speed, output quality, context size, and VRAM usage.

  • 24GB VRAM makes 20B-class models like gpt-oss:20b the practical sweet spot on the RX 7900 XTX.
  • The gpt-oss:20b model achieves ~60-70+ tokens per second with very strong quality assessments.
  • Use the benchmark, hardware, and model browser pages to verify the recommendation you pick.
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Start with the data

These guides stay grounded in the same benchmark pages the rest of the site uses, so you can move from the recommendation to the raw results without leaving the app.

Benchmark resultsBrowse modelsHardware browser