MacBook Air M5 24GB has 24GB of unified memory and is a strong fit for portable everyday local AI. These recommendations are generated from the current LocalClaw catalogue and filtered for realistic memory headroom.
Current model · Apple specifications verified August 25, 2026
Yes. With 24GB of unified memory, MacBook Air M5 24GB fits 138 current LocalClaw catalogue models under the conservative 8k-context memory filter. Start with Granite 4.2 (8B). Apple lists this as a current Mac configuration. This is memory-fit guidance, not a hands-on speed benchmark.
Hardware facts come from Apple. LocalClaw separately calculates catalogue compatibility from unified memory and model requirements. No unreleased Mac performance result is inferred from chip specifications.
Yes. With 24GB of unified memory, MacBook Air M5 24GB fits 138 current LocalClaw catalogue models under the conservative 8k-context memory filter. Start with Granite 4.2 (8B). Apple lists this as a current Mac configuration. This is memory-fit guidance, not a hands-on speed benchmark.
This configuration has 24GB of unified memory. Apple lists up to 32GB for the M5 family represented here. LocalClaw reserves memory for macOS, the runtime and an 8k context before marking a model compatible.
Apple lists this model in its current product and technical specifications pages.
No. The compatibility count and ranking are calculated from 24GB of unified memory and the current LocalClaw catalogue. They are not measured tokens-per-second results or hands-on benchmarks.
Start with Granite 4.2 (8B) on this Mac. A comfortable or good fit leaves useful memory for macOS and your local runtime. A tight fit can still work, but close other apps, reduce context length when needed, and prefer the listed quantization.
IBM Granite 4.2 8B instruct model with Apache 2.0 weights, 128K context, thinking-mode chat template, tool calling and practical GGUF plus MLX paths for everyday local machines.
Official MIT reasoning model from Ornith AI with a 262K native context window, tool-calling focus, and official Q4_K_M GGUF, MLX and Ollama/llama.cpp paths for local coding-agent experiments on 16GB+ machines.
Official OpenBMB compact on-device LLM with Apache 2.0 licensing, 131K context, tool-calling and coding focus, plus official Q4_K_M GGUF, Ollama, llama.cpp, Docker and OpenClaw run paths.
Liquid AI hybrid model built for on-device assistants. 8.3B total / 1.5B active, 128K context, tool use, GGUF, ONNX, MLX, llama.cpp and LM Studio support. Open-weight under LFM 1.0.
Established 14B Qwen 3 model for reasoning, coding and chat. Its Q4 build is a tight fit on many 16GB machines, so context length and system headroom matter.
IBM Granite 4.2 3B is the compact Apache 2.0 Granite reasoning model with 128K native context, thinking-mode chat, tool calling and official GGUF artifacts for laptop-class local inference.
Liquid AI edge vision-language model with LFM2.5-2.6B backbone, SigLIP2 NaFlex vision encoder, 32K context, LFM 1.0 open weights and official GGUF plus llama.cpp and MLX runtime paths for local image chat and OCR.
Official InclusionAI MIT hybrid-reasoning MoE with 131K context, 1.3B active parameters and a validated GGUF path through stock llama.cpp builds from the 2026-08-17 bailingmoe3 merge.
Liquid AI compact hybrid model with 128K context, LFM 1.0 open weights, official GGUF, ONNX and MLX artifacts, and practical llama.cpp / LM Studio paths for 8GB-class local machines.
The shared LocalClaw engine first rejects hosted-only, excluded and oversized records. It reserves system and 8k-context headroom, labels comfortable, good and tight fits, then ranks the remaining models by hardware fit, use case, catalogue capability ratings, runtime and freshness. Community stars are never included. This is practical guidance, not a standardized third-party benchmark.
This guide is about local AI fit, not live pricing. Prices and availability change. An Amazon link may be an affiliate link that supports LocalClaw at no extra cost.