Either one of these!
Specifically CPU: M1 Max. RAM: 32 GB. Either the Mac Studio desktop or MacBook Pro 16" with that CPU and RAM is a dream setup.
Why? For my chess development. I don't mean my own chess playing skills - something I've given up improving years ago. It's the chess website and tools. Currently I'm building them using a monthly subscription to Google AI Pro.
Check out Catur.org for what has been completed so far.
LLM Models
For web application coding (Laravel, PHP 8.3+, JavaScript, Vue/React, HTML/CSS),
you want to focus on medium-weight models quantized to 4-bit (Q4_K_M):
| Model | VRAM / RAM Usage | Expected Speed | Use Case & Performance |
|---|---|---|---|
| Qwen 2.5 Coder 14B | ~9 GB | 25–30 tok/s (Fast) | The Sweet Spot. Excellent at PHP, JS, and full-stack web structure. Leaves ~13GB of RAM free for local servers, Docker, and IDEs. |
| Qwen 2.5 Coder 32B | ~20 GB | 10–14 tok/s (Usable) | Maximum Code Quality. Near GPT-4o quality for refactoring and complex logic. Runs close to memory limits. |
| DeepSeek-R1 14B / 32B | ~9 GB / ~20 GB | 8–18 tok/s | Best for debugging tricky architectural bugs or algorithm logic via step-by-step reasoning. |
| Codestral 22B | ~14 GB | 18–22 tok/s | Extremely strong multi-language code generation designed specifically for IDE completion. |
AI Recommendation: Daily drive Qwen 2.5 Coder 14B inside your IDE for instant completions, and switch to Qwen 2.5 Coder 32B when you need heavy refactoring or architectural planning.
Am I planning to get either the Mac Studio M1 Max or MacBook Pro M1 Max? Not in the immediate future, as I can't afford to replace my broken camera for the coming 2026 festival. And also because the notebook costs RM 5500 used. Mac Studio is the cheapest at RM 4800 used. But the intention is certainly there!
Interesting project suggested by Deepseek AI:
| Project Type | Feasibility | Why It Works on M1 Max + 32GB |
|---|---|---|
| UCI engine wrappers / launchers | ✅ Very easy | Zero AI/ML needed—just process management |
| Local LLM-powered chess commentary | ✅ Great fit | 14B–32B models run locally (see your model table) |
| Chess puzzle generator with NLP | ✅ Great fit | LLMs generate natural-language puzzle descriptions |
| Interactive opening trainer | ✅ Great fit | Lightweight + LLM can explain lines |
| Chess position search engine (semantic) | ✅ Good | Embeddings + vector DB fit in 32GB |
| Tactics tutor with mistake analysis | ✅ Good | Use local LLM to explain blunders |
| Custom chess engine (reinforcement learning) | ⚠️ Possible but slow | Training is GPU-heavy—M1 Max is okay for inference, not SOTA training |
| Full Leela Chess Zero clone | ❌ Not viable | Needs multiple high-end GPUs |
| Online chess platform with AI analysis | ✅ Very doable | Laravel/PHP backend + local analysis service |
Leave a Comment