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Apple Silicon AI Calculator

Pick your Mac. See exactly which AI models fit in your unified memory, the expected tokens-per-second throughput, and whether MLX, Ollama, or llama.cpp is the right runtime for your hardware. Covers M1 through M4, all RAM tiers, all standard quantization levels.

📅 Published: May 9, 2026🔄 Last Updated: May 9, 2026✓ Manually Reviewed

Usable VRAM

72 GB

~75% of 96 GB unified · macOS reserves the rest

Memory bandwidth

400 GB/s

~320 GB/s effective for inference

Recommended

Qwen 2.5 32B

~14 tok/s at Q4_K_M

ModelSize at Q4_K_MFits?Est. tok/sNotes
Llama 3.2 3B1.9 GB✓~143Edge / mobile / on-device
Phi-4 Mini 3.8B2.4 GB✓~113Reasoning at edge
Gemma 3 4B2.6 GB✓~105Google small model
Mistral 7B4.2 GB✓~65Battle-tested 7B baseline
Llama 3.1 8B4.7 GB✓~58Most-deployed open 8B
Qwen 2.5 14B8.2 GB✓~33Strong general 14B
Phi-4 14B8.2 GB✓~33Best small reasoning model
Mistral Small 22B13.0 GB✓~21Mistral mid-tier
Gemma 3 27B16.0 GB✓~17Strong general-purpose 27B
Qwen3.6-27B16.0 GB✓~17Dense 27B beating older 397B
Qwen 2.5 32B19.0 GB✓~14Solid mid-size dense
Llama 3.3 70B42.0 GB✓~6Most-deployed open 70B
Llama 3.1 70B42.0 GB✓~6Long-context 70B baseline
Qwen 2.5 72B43.0 GB✓~6Top open dense 72B
Mistral Large 273.0 GB✗—123B dense multilingual
DeepSeek V3 (671B MoE)380.0 GB✗—671B MoE / 37B active
DeepSeek V4-Pro (1.6T MoE)900.0 GB✗—Current open frontier — needs M3 Ultra 512GB at ~Q4 only
Kimi K2.6 (1T MoE)575.0 GB✗—1T MoE / 32B active

MLX vs Ollama vs llama.cpp on this Mac

For 96GB+ Macs running 70B+ models, MLX-LM often gives the best throughput — 10-30% faster than llama.cpp Metal on M-series. Ollama remains the easiest path. For 256GB+ M3 Ultra running DeepSeek V3/V4: llama.cpp Metal at Q4_K_M, ~10-15 tok/s.

Estimates use llama.cpp Metal benchmarks calibrated against published numbers from Hugging Face and Reddit r/LocalLLaMA threads. Actual throughput varies ±20% by exact model architecture and macOS version. Larger models may exceed estimates if they fit comfortably; long context reduces throughput proportionally.

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Why Apple Silicon is interesting for local AI

The unified memory architecture is the key. On a Mac Studio M3 Ultra 192GB, the GPU can directly access up to 144GB for model weights — more than any single NVIDIA consumer GPU and competitive with H100 80GB on capacity (though slower per token). For inference workloads where you want to run a 70B model on a quiet desktop with no separate GPU box, no PSU upgrade, and no driver fiddling, Apple Silicon is the cheapest path that exists.

The trade-offs: per-token compute throughput is lower than NVIDIA discrete GPUs (M3 Ultra ~800 GB/s bandwidth vs H100 ~3.35 TB/s), so a 7B model on a $1,600 RTX 4090 will out-throughput a 7B model on a $4,000 Mac Studio. The win zone is 32B-200B models, where the Mac\'s memory capacity matters more than its per-token speed.

Frequently asked questions

Is Apple Silicon actually good for running LLMs?
Surprisingly yes — for inference, especially at 32B+ model sizes. The unified memory architecture means a Mac Studio with 192GB can serve a 70B model that no single discrete GPU can. The trade-off: per-token compute is lower than NVIDIA discrete GPUs, so small-model throughput (7B-13B) is below what an RTX 4090 delivers. The win zone for Apple Silicon: 32B-200B models that need lots of memory but where you only need 10-30 tok/s for personal/dev use.
M3 Max 128GB vs M3 Ultra 192GB — which is better for AI?
M3 Ultra wins on throughput (800 GB/s memory bandwidth vs 400 GB/s) and capacity (192GB vs 128GB), but costs 2x more. If you only run models that fit in 96GB (Llama 3.1 70B at Q4 + 32K context), M3 Max is the better value. If you want to run DeepSeek V3 / Qwen 72B BF16 / multiple models concurrently / large MoE configs, the M3 Ultra 192GB or 256GB is the right call. M3 Ultra 512GB unlocks DeepSeek V4 territory but costs $10K+ and is overkill for most users.
How do these tokens-per-second estimates compare to real benchmarks?
The estimates use a simple bandwidth-bound model: throughput ≈ 0.85 × effective_bandwidth / model_size_GB. Calibrated against published llama.cpp Metal benchmarks on Reddit r/LocalLLaMA, Hugging Face leaderboards, and our own Mac runs. Accuracy is ±20% — actual numbers depend on exact model architecture, macOS version, prompt length, and KV cache size. Long context (32K+) drops throughput proportionally because KV cache reads dominate.
MLX vs Ollama vs llama.cpp on Mac — which should I use?
Ollama for ease (one command install, model registry, OpenAI-compatible API). MLX for fine-tuning and PyTorch-like APIs (Apple's native framework, often 10-30% faster on 70B+ models). llama.cpp directly when you want bleeding-edge quantization formats or need maximum control. For 95% of users on 32GB+ Macs: install Ollama and stop thinking about it. For 96GB+ M3 Max/Ultra running 70B+ models: try MLX-LM if you want maximum throughput.
How much memory does macOS reserve from unified memory?
macOS reserves roughly 25% of unified memory for the OS, app framework, KV cache overhead, and headroom. On a 64GB Mac, expect ~48GB usable for model weights + KV cache. On a 192GB Mac, ~144GB usable. You can push past this with `sudo sysctl iogpu.wired_limit_mb` to increase the GPU memory cap, but this risks system instability if the OS hits memory pressure mid-inference.
Can I fine-tune models on Apple Silicon?
Yes for small-to-medium models. MLX-LM supports LoRA / QLoRA fine-tuning natively. On M3 Max 128GB, you can QLoRA-tune up to a 70B model. Throughput is slower than discrete GPUs (1-3 tok/s training throughput vs 30-50 on H100), so plan for hours-to-days runs rather than minutes. For serious fine-tuning workloads at 70B+, cloud H100s are still more cost-effective. For experimentation, prototyping, and 7B-32B fine-tuning: Apple Silicon works well.
Why does my Mac get slower over long inference sessions?
Three causes. (1) Thermal throttling — sustained inference heats the chip, after 5-15 minutes the SoC down-clocks. M3 Pro / M4 Pro throttle harder than M3 Max / M3 Ultra (better cooling). (2) KV cache growth — long context fills more memory, KV reads dominate decode, throughput drops. (3) Memory pressure — if other apps allocate memory, inference can spill to swap, killing throughput. Mitigations: close other apps, use external cooling for long runs, cap context to what you actually need.
Will the M5 / M5 Max / M5 Ultra change this picture?
Expected late 2026. Rumors suggest M5 Max/Ultra will push memory bandwidth to 1.0+ TB/s and add native FP8 tensor cores via the Neural Engine. If accurate, that would close most of the per-token throughput gap with H100/H200 for inference, while preserving the 192GB+ unified memory advantage. We'll update this calculator within a week of any M5 launch.

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The Local AI Deployment course covers MLX, Ollama, Metal, memory management, and fine-tuning on Apple Silicon — including the M3 Ultra / M4 Max workflows that this calculator hints at. First chapter free, no card required.

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Written by Pattanaik Ramswarup

Creator of Local AI Master

I build Local AI Master around practical, testable local AI workflows: model selection, hardware planning, RAG systems, agents, and MLOps. The goal is to turn scattered tutorials into a structured learning path you can follow on your own hardware.

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