About this video
👉 Work like an expert. Always on the latest OpenClaw. Same models, lower bill. → https://myclaw.ai/?utm_source=yt-samuelgregoryd3 Your high-end laptop is failing you because 32GB of RAM is no longer enough for modern AI development. This video explores the brutal reality of trying to run local AI models like Qwen 3.5 and Gemma 4 on the M5 MacBook Pro for coding tasks. We dive into the trade-offs between quantisation, context size, and raw performance. Key Takeaways: - Context size is the ultimate bottleneck for local AI coding. - 32GB of RAM is insufficient for Mixture of Expert models with functional context windows. - MLX offers superior speed over GGUF but cannot bypass physical memory limits. - Small 9B models lack the reasoning capabilities for complex debugging. - The entry-level M5 MacBook Pro is not recommended for serious local AI workflows.
The M5 MacBook Pro Is A Local AI Failure
Your shiny new M5 MacBook Pro is essentially a paperweight if you intend to use it for serious local AI development. Despite the marketing hype surrounding Apple's latest silicon, my extensive testing with 32GB of RAM proves that entry level hardware simply cannot keep up with the demands of modern Mixture of Expert models.
The RAM Wall
The primary issue is not the raw speed of the M5 chip, but the architectural limitations of memory. When running models like Qwen 3.5 or Gemma 4, you are forced to choose between model intelligence and context size. To get any meaningful work done, you need a context window of at least 100,000 tokens. On a 32GB machine, once you load a 4-bit quantised model, there is virtually no room left for the KV storage required for that context.
GGUF vs MLX
The performance gap between different backends is staggering. While MLX provides results in seconds, it still hits the same physical RAM ceiling. GGUF, on the other hand, was so slow that I left a single coding task running for over 12 hours only to find it still had not finished by morning. If you are waiting half a day for a shell script, your workflow is fundamentally broken.
The 9B Fallacy
Some might suggest using smaller 9 billion parameter models to save memory. However, these models lack the logical depth required for complex, agentic coding tasks. In my tests, the 9B models failed to understand basic error logs from Vercel, making them useless for professional development.
Final Verdict
Do not buy the base M5 MacBook Pro for AI. If you are serious about local LLMs, you must upgrade to the M5 Pro or Max with significantly more RAM. Alternatively, consider a setup where the model runs on a dedicated local server while you code on a lighter machine. The M5 MacBook Pro simply does not get a vote from me for this specific use case.