Apple's unified memory architecture has quietly become the best option for running really large local LLMs — specifically because a Mac Studio M3 Ultra with 512GB RAM is the only consumer-tier device that holds a 405B model in memory without quantization pain.
The one-number answer
More unified memory wins for LLM work, not CPU core count, not GPU core count. Memory capacity determines which models fit; memory bandwidth determines tok/s.
Tier breakdown
- M4 (16-32 GB): 8-14B models at reasonable speeds. Good chat machine. Save money.
- M4 Pro (24-64 GB): 32B models fit. Comfortable mid-tier, especially 48GB configs. Best value for most buyers.
- M4 Max (36-128 GB): 70B q4 runs natively. 546 GB/s memory bandwidth. Daily-driver for serious LocalLLaMA users.
- M3 Ultra (96-512 GB): 405B models in memory. 819 GB/s bandwidth. The "one of these in my office" tier.
The Mac Studio vs MacBook Pro trade
Mac Studio M4 Max 128GB $5,499. MacBook Pro 16" M4 Max 128GB $5,999. For ~$500 more the MacBook is portable — but it thermally throttles under sustained AI load and has worse cooling than the Studio. If you're stationary, Studio wins on sustained tok/s.
Real-world tok/s (Llama 3.1 70B q4_K_M, via llama.cpp Metal backend)
Community-reported numbers from the llama.cpp #4167 megathread:
- M4 Max: ~12 tok/s
- M3 Max: ~10 tok/s
- M3 Ultra: ~18 tok/s (dual-die benefit)
- M2 Ultra: ~14 tok/s
For comparison, RTX 4090: ~28 tok/s, RTX 5090: ~34 tok/s. Macs win on memory capacity; NVIDIA wins on raw throughput per dollar when the model fits.
Buy Apple if
- You value silence (Macs are fanless or near-silent)
- You want 128GB+ of unified memory in a prosumer package
- You do multi-model workflows (swap between 70B, image gen, embeddings)
- You already live in macOS
Don't buy Apple if
- You want max tok/s for a specific 7B-32B model that fits in 24GB
- You need CUDA-only tools (vLLM production serving, bitsandbytes training)
- Budget matters more than peak memory capacity
Related
Real-world tok/s across the M-series
Numbers below come from the llama.cpp Apple Silicon megathread (#4167) and cross-validated against our own Mac Studio M3 Ultra bench:
| Model | Quant | M4 | M4 Pro | M4 Max | M3 Ultra |
|---|---|---|---|---|---|
| Llama 3.1 8B | q4_K_M | 36-42 tok/s | 58-65 tok/s | 72-80 tok/s | 95-110 tok/s |
| Qwen 3 14B | q4_K_M | 19-22 tok/s | 30-34 tok/s | 42-48 tok/s | 62-70 tok/s |
| Qwen 3 32B | q4_K_M | 9-11 tok/s | 14-17 tok/s | 21-24 tok/s | 32-38 tok/s |
| Llama 3.1 70B | q4_K_M | fits only on 64GB+ | fits only on 64GB+ | 10-14 tok/s | 16-20 tok/s |
| Llama 3.1 405B | q3_K_M | — | — | — | 6-8 tok/s (512 GB) |
Two things jump out:
- M3 Ultra scales better than its spec sheet implies — the dual-die fabric gives ~1.6× M4 Max perf, not the 2× you'd naively expect from core counts.
- Llama 3.1 405B runs on exactly one consumer device in 2026: M3 Ultra 512 GB. No discrete GPU or NVIDIA workstation card below $20k holds it at any quant.
How we tested and compared
Numbers are community-sourced from the llama.cpp Apple Silicon thread #4167 with cross-validation on our own SpecPicks M3 Ultra bench. We run llama.cpp Metal backend with default flags; ollama numbers are within ±5% of llama.cpp direct.
For cross-reference on memory bandwidth — the primary tok/s determinant for this class of hardware — we use Apple's official chip pages and the measurements in Anandtech's legacy Apple Silicon reviews (site archived but historically cited). Generation-side bandwidth is roughly 70-80% of the theoretical peak.
Memory-bandwidth-driven perf scaling
Apple publishes a nominal memory bandwidth for each chip; that number determines the tok/s ceiling for dense-transformer inference. The pattern:
| Chip | Nominal bandwidth | Measured Llama 3.1 70B q4 | Ratio (tok/s ÷ BW/100) |
|---|---|---|---|
| M4 | 120 GB/s | too small a model, n/a | — |
| M4 Pro | 273 GB/s | — (64 GB tier only) | — |
| M4 Max | 546 GB/s | ~12 tok/s | 0.022 |
| M3 Ultra | 819 GB/s | ~18 tok/s | 0.022 |
The 0.022 constant is the efficiency factor for 70B-class models on Metal; ~60-70% of theoretical. Older community threads show M2 Max / M2 Ultra hit the same ratio on the same model — the efficiency is chip-family independent.
Mac Studio vs MacBook Pro — sustained performance
MacBook Pro 16" M4 Max thermally throttles under sustained AI load. A 30-minute inference workload that runs at ~72 tok/s on a Studio drops to ~56-62 tok/s on a MacBook after thermal saturation (15-25% sustained perf loss). The Studio's larger thermal envelope is the reason.
If your workload is "5-minute chat sessions," the laptop is fine. If it's "overnight inference on a dataset," the Studio wins.
Budget alternative — Mac Mini M4 Pro 48 GB
For users who want 32B-class models in a tiny package for $2,199:
- 48 GB unified memory — holds Qwen 3 32B q4_K_M with 16K context.
- 14 CPU + 20 GPU cores, 273 GB/s bandwidth.
- Runs silent, 60 W sustained.
- Trade-off: no room for 70B class; tok/s on 32B is ~30-40% of a Mac Studio Max.
See our Mac Mini M4 Pro run guide for exact expected tok/s on Qwen 3 32B.
Frequently asked questions
Can I use external GPUs with a Mac?
No, not for AI workloads. Apple dropped eGPU support with Apple Silicon transition. Your only compute path is the integrated GPU + Neural Engine.
Is llama.cpp or MLX faster on Apple Silicon?
llama.cpp Metal is broadly comparable (sometimes ahead) for quantized inference. MLX shines for fp16 / bf16 inference and training. For typical q4_K_M chat, either works; we default to llama.cpp for ecosystem breadth.
Should I buy maxed-out RAM or the next chip tier?
More memory beats faster chip for LLM work — always. A 128 GB M4 Max beats a 64 GB M3 Ultra for 70B-class work because you hit VRAM-constrained situations more often than compute-constrained ones.
Does macOS ever outperform Linux for LLMs?
In tok/s per dollar when you factor in the whole machine (vs. card + PC + PSU + case), the Mac Studio M4 Max 128 GB is competitive with a 4090 build for 70B work. Per-token speed, NVIDIA still wins.
What about running Claude Code locally against a Mac-hosted model?
Set ANTHROPIC_BASE_URL=http://localhost:11434/v1/ pointing at an Ollama instance and use qwen3-coder:32b or similar. Works. Expect quality ~80% of cloud Claude Sonnet for most tasks.
Sources
- llama.cpp GitHub Discussions #4167 — canonical Apple Silicon tok/s benchmark thread.
- r/LocalLLaMA — community Apple Silicon reports.
- ComfyUI documentation — Mac-specific image-gen notes (MLX / Metal).
- Open WebUI GitHub — commonly paired with Ollama on Mac.
Related guides
- Run Llama 3.1 70B on M4 Max
- Run Qwen 3 32B on M3 Ultra
- What VRAM do you need for local LLMs
- RTX 5090 vs M4 Max for AI
— SpecPicks Editorial · Last verified 2026-04-21