I run a local AI stack full-time on a MacBook Pro M3 Max with 128GB of unified memory. Over the past few months I've tested more than 10 models using oMLX benchmarks and daily real-world use. This guide gives you the actual numbers — prompt processing speed, generation speed, quality scores — and tells you which model to run depending on your situation.
The short answer: MoE (Mixture of Experts) models are the 2026 story. Architecture matters more than parameter count. A 35B MoE model with 3B active parameters runs faster than a 7B dense model and thinks better than a 27B dense model. Once you understand that, the choice becomes obvious.
Speed Benchmarks — What Actually Runs Fast
The chart below shows generation speed (tokens per second) — the number that determines how fast answers appear on your screen. Above 20 tok/s feels instant. Below 10 tok/s feels slow.
Quality Benchmarks — Which Models Actually Think
Speed without quality is useless. Here are the quality scores from official benchmarks and model papers, mapped to the models tested on M3 Max:
| Model | MMLU-Pro | GPQA | HumanEval | MATH-500 | My Score |
|---|---|---|---|---|---|
| Qwen3.5-27B🏆 | 86.1 | 85.5 | 84.8 | 88.0 | 90 |
| Gemma-4-26B MoE | 82.6 | — | 77.0 | 82.0 | 80 |
| Qwen3.5-35B MoE | ~86 | ~85 | ~85 | ~88 | 88 |
| DeepSeek-R1-8B | — | 49.0 | 70.0 | — | 75 |
| Llama-3.3-70B | — | 50.5 | 88.4 | 77.0 | 70 |
| Phi-4-mini-reasoning | — | 56–69 | — | 91.8 | 68 |
| Devstral-24B | — | — | 82.0 | — | 65 |
My Recommendations by Use Case
What to Avoid
Llama-3.3-70B — 7.7 tok/s is painful for interactive use. The quality bump over smaller models doesn't justify the speed penalty on M3 Max. Skip it unless you're batch processing.
Mixtral-8x22B — An older MoE that's been entirely outclassed by Qwen3.5 and Gemma-4. Slower, lower quality, larger on disk. Nothing to recommend it in 2026.
Any model >40B dense parameters — Dense 70B+ models run below 8 tok/s on M3 Max. You'll be staring at a blinking cursor. MoE is the answer, not bigger dense models.
Why MoE Models Are the 2026 Story
The single most important thing I learned from these benchmarks: Mixture of Experts architecture changes everything.
A dense 27B model activates all 27 billion parameters for every single token it generates. A MoE 35B model activates only 3B parameters per token — routing each token to the most relevant "expert" subset. The result: you get 35B-level reasoning at 3B-level speed, using 3B-level memory bandwidth.
This is why Qwen3.5-35B-A3B runs at 47.6 tok/s despite having more total parameters than the 27B model that runs at 22 tok/s. The "35B" is the total capacity. The "A3B" (Active 3B) is what's running at inference time.
Gemma-4-26B-A4B follows the same pattern — 26B total, 4B active, 41.5 tok/s. This will be the dominant local AI architecture for the next few years. When choosing a model, look for "MoE" or "A[N]B" in the name.
Context Length Performance
Generation speed drops as context length increases — the model has to attend to more tokens. Here's how Gemma-4-26B degrades across context sizes:
| Context | PP tok/s | TG tok/s | Notes |
|---|---|---|---|
| 1k | 937 | 41.5 | Peak speed — daily use |
| 4k | 1,174 | 40.1 | Still excellent |
| 16k | 1,015 | 30.8 | Good for long documents |
| 32k | 955 | 24.5 | Acceptable for book-length input |
| 64k | 754 | 15.5 | Starts to feel slow |
| 128k | 534 | 5.6 | Painful — use sparingly |
Final Verdict
Local AI on Apple Silicon has crossed a usability threshold in 2026. The combination of MoE architecture and Apple's unified memory bandwidth makes it genuinely competitive with cloud models for most tasks — at zero cost per query, full privacy, and no rate limits.
My daily stack: Qwen3.5-35B-A3B as the main brain, Devstral-24B for code, DeepSeek-R1-8B for quick lookups. Total RAM usage: ~50GB, leaving 78GB free for other work.
If you want to set up this stack from scratch, read the companion guide: How to Run a Full Local AI Stack on Your Mac.
