Every Mac That Can Run Local AI Coding Models
I mapped four coding models to every relevant Mac Studio configuration. Here's what fits, how fast it runs, and what you should actually buy.
The question I keep seeing: “which Mac do I need for local AI coding?”
The answer depends entirely on which model you want to run. I benchmarked four models and tested their agentic capabilities in the previous two posts. This post maps those results to hardware.
The Table
Model sizes at Q4_K_M quantization (what you’d actually run via Ollama or llama.cpp). Speeds are estimated useful tokens per second, meaning the tokens that appear as code on your screen, not thinking overhead.
| Model | Q4 Size | M4 Max 64GB | M4 Max 128GB | M3 Ultra 256GB | M5 Ultra 256GB |
|---|---|---|---|---|---|
| Gemma 4 31B | 19GB | 6 tok/s | 6 tok/s | 9 tok/s | 14 tok/s |
| Qwen3.5-27B | 16GB | 7 tok/s | 7 tok/s | 10 tok/s | 16 tok/s |
| Coder-Next 80B | 48GB | 35 tok/s* | 35 tok/s | 52 tok/s | 78 tok/s |
| Devstral 2 123B | 75GB | — | 6 tok/s* | 8 tok/s | 12 tok/s |
| MiniMax M2.5 | 138GB | — | — | 30 tok/s | 45 tok/s |
-- = doesn’t fit. * = tight fit, limited context window.
The machines:
| Config | RAM | Bandwidth | Price | Availability |
|---|---|---|---|---|
| Mac Studio M4 Max | 64GB | 546 GB/s | $2,899 | Ships in days |
| Mac Studio M4 Max | 128GB | 546 GB/s | $3,699 | Ships in days |
| Mac Studio M3 Ultra | 256GB | 819 GB/s | $5,999 | 4-5 month backorder |
| Mac Studio M5 Ultra | 256GB | ~1,228 GB/s | ~$5,999+ | Expected June 2026 |
Speed projections use memory bandwidth ratios from measured RTX PRO 6000 GPU data. Expect 10-20% variance on real hardware. MacBook Pro M5 Max can also do 128GB but starts at $5,099 for $1,400 more than the Mac Studio. For sustained AI workloads, the desktop is the better buy.
What To Buy
For fast autocomplete and simple code gen: M4 Max 64GB ($2,899).
Coder-Next at 35 tok/s for speed, or Gemma 4 at 6 tok/s for reliability. Coder-Next is fast but inconsistent (13/15 code execution, 13/30 tool calling). Gemma 4 is slower but got a perfect 15/15 on code execution and 20/30 on tool calling. Pick your tradeoff. Ships in days.
For a balance of quality and speed: M4 Max 128GB ($3,699).
Same Coder-Next speed, plus you can swap to Devstral 123B (6 tok/s, tight fit) when you need higher quality on a harder problem. Having options matters. Ships in days.
For agentic coding: M5 Ultra 256GB (~$5,999+, expected June 2026).
This is the only config that runs MiniMax M2.5: 45 tok/s, 80.2% SWE-bench (within 0.6 points of Claude Opus), and the best agentic scores in my testing (22/30 on tool calling). After loading the 138GB model, you’ve got ~110GB left for KV cache, which translates to 128-200K tokens of context. That’s enough to load a medium codebase and have a long working session without compaction. It’s also the highest-quality coding model you can run locally at a usable quantization. MiMo-V2-Flash (309B) fits at Q4_K_M (187GB) with ~70GB for KV cache, but scores 73.4% on SWE-bench versus MiniMax’s 80.2%. GLM-5.1 and Kimi K2.5 can technically squeeze into 256GB at aggressive 2-bit quantization (~236-240GB), but with almost no context headroom and degraded quality. MiniMax at Q4 with 110GB of KV headroom is the only one that’s both high quality and actually comfortable to use.
The M3 Ultra 256GB ($5,999) technically works today at 30 tok/s, but Apple’s delivery estimate is 4-5 months. If you order now, it arrives around August or September, by which point the faster M5 Ultra will likely be shipping. Apple killed the 512GB config in March due to DRAM shortages and raised the 256GB upgrade by $400. High-RAM Macs are hard to get right now.
The price jump from $3,699 to $5,999 is $2,300. That’s not about “more memory.” It’s about crossing from a fast code generator to a capable coding agent. Whether that’s worth it depends on your workflow. For me, it’s mostly agentic. So I’m waiting for the Ultra.
Why This Gets Better Over Time
Most hardware posts stop at today’s benchmarks. But the machine you buy today runs better models next year.
Open-source coding models on SWE-bench Verified:
| When | Best Open Model | Score |
|---|---|---|
| July 2025 | Kimi K2 | 65.8% |
| Early 2026 | Kimi K2.5 | 76.8% |
| April 2026 | MiniMax M2.5 | 80.2% |
In under a year, from 65.8% to 80.2%. The gap to Claude Opus (80.8%) is now 0.6 points.
The models are also getting more efficient. The MoE architecture trend means smarter models with fewer active parameters per token:
- Kimi K2 (July 2025): 1T total, 32B active
- MiniMax M2.5 (2026): 229B total, 10B active. Same quality tier, one-third the active compute.
- Qwen3-Coder-Next (Feb 2026): 80B total, 3B active. Competitive on basic coding with a fraction of the resources.
Fewer active parameters means faster generation at the same bandwidth. If the next model hits 85% SWE-bench with 8B active params, the M5 Ultra runs it at 55-60 tok/s. Better and faster, same hardware.
Quantization is improving too. Q4_K_M retains 90%+ of full-precision quality on coding tasks, and that gap keeps shrinking with techniques like Unsloth’s dynamic quantization.
By late 2026, the next generation of MoE models should push into 85%+ territory. By 2027, if the trend holds, open models match today’s Opus on the same $6K machine. No subscription, no rate limits, no peak-hour throttling.
You’re not buying today’s performance. You’re buying a platform that gets better every quarter.
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