Apple Silicon for Local LLMs — Mac Buyer's Guide 2026
AI helped me draft this Apple Silicon round-up; every UMA bandwidth and tokens-per-second number was hand-checked against the MLX repo and the XiongjieDai community runs cited below.
Updated May 2026 · Covers M4, M4 Pro, M4 Max · Ollama, LM Studio, llama.cpp, MLX
Apple Silicon Macs are the most practical way to run large language models locally on a single device. Unified memory means the GPU has direct access to all system RAM at 120–800 GB/s — no PCIe bottleneck, no separate VRAM limit. The base Mac mini M4 handles 7B models. The Mac Studio M4 Max handles 70B. This guide covers which chip to buy, why the architecture matters, and how to get running with the best tools.
Quick picks by model size
- 7B models — Mac mini M4 16GB — cheapest Apple Silicon entry point
- 13B models — Mac mini M4 24GB — same chip, fits 13B at Q4_K_M
- 70B models — Mac mini M4 Pro 48GB — cheapest 70B device; 48 GB fits Llama 3.3 70B at Q4_K_M
- 70B models — Mac Studio M4 Max 64GB — only practical single-device option
- 70B at FP16 / 100B+ — Mac Studio M4 Max 128GB — research and multi-model setups
Why Unified Memory Changes the Equation for LLMs
Apple Silicon uses unified memory — RAM and VRAM are the same pool. A Mac Studio M4 Max with 64GB can run 70B models at Q4, something that requires two RTX 4090s on PC. Bandwidth is 400–800 GB/s, matching or beating discrete GPUs at fraction the power draw.
On a traditional PC, your GPU has its own dedicated VRAM — 8, 16, or 24 GB on a consumer card. The CPU cannot use GPU VRAM, and vice versa. When you run an LLM that is larger than VRAM, the inference engine has to split layers between GPU and CPU RAM, crossing the PCIe bus each time. PCIe 5.0 x16 maxes out at roughly 64 GB/s in one direction. That bottleneck tanks inference speed whenever any part of the model spills out of VRAM.
Apple Silicon uses a unified memory architecture (UMA): the CPU, GPU, and Neural Engine all share the same physical memory pool, connected by Apple's memory fabric. On the M4 Max, that fabric runs at 400 GB/s — 6x the theoretical maximum of PCIe 5.0. On the M4 Ultra (used in Mac Pro), it doubles again to 800 GB/s.
For LLMs, this means every gigabyte of RAM in the machine is usable as "VRAM" without any bandwidth penalty. A Mac with 64 GB of unified memory can load a 70B model at Q4_K_M (~37 GB) and run inference on it at full GPU bandwidth. No PCIe transfers. No layer offloading. Just fast matrix multiplications on the GPU cores.
| Hardware | Memory type | Bandwidth | Max model (single device) | Notes |
|---|---|---|---|---|
| RTX 4060 8GB | GDDR6 VRAM | 272 GB/s | 7B Q4_K_M | Budget entry, limited capacity |
| RTX 4060 Ti 16GB | GDDR6 VRAM | 288 GB/s | 13B Q8 | Best VRAM/dollar NVIDIA |
| RTX 4070 Ti Super 16GB | GDDR6X VRAM | 672 GB/s | 13B Q8 | 2.3x faster than 4060 Ti |
| RTX 4090 24GB | GDDR6X VRAM | 1,008 GB/s | 34B Q4_K_M | Fastest consumer GPU bandwidth |
| RTX 5090 32GB | GDDR7 VRAM | 1,792 GB/s | 34B Q8 | Top consumer GPU in 2026 |
| M4 Mac mini (any) | Unified (LPDDR5X) | 120 GB/s | 13B Q4_K_M | Base M4 chip — lower bandwidth |
| M4 Pro Mac mini | Unified (LPDDR5X) | 273 GB/s | 34B Q4_K_M | Pro chip — 2.3x more bandwidth |
| M4 Max Mac Studio 64GB | Unified (LPDDR5X) | 400 GB/s | 70B Q4_K_M | Only consumer device for 70B |
| M4 Max Mac Studio 128GB | Unified (LPDDR5X) | 400 GB/s | 70B FP16 | Unique: 70B at full precision |
Note: raw GB/s is only part of the story. NVIDIA discrete GPUs have extremely fast VRAM but are limited to their dedicated VRAM pool. Apple's unified memory is slower per GB/s on the top consumer cards but is available in much larger pool sizes, which is what matters for 70B inference. Use the VRAM Calculator to check any specific model's requirements.
Buy MacBook Pro M4 Pro on AmazonApple Silicon Chips: Model-by-Model Breakdown
Mac mini M4 — 16 GB unified memory
Entry PointMac mini M4 — 16 GB unified memory
M4 · 10-core GPU
Memory
16
Bandwidth
120 GB/s
Max model
7B at Q4_K_M
Speed
20–30 t/s at 7B
Pros
- + Cheapest Apple Silicon LLM device
- + Runs 7B models at Q4_K_M with ~4 GB headroom
- + Completely silent, 30 W idle
- + Works with Ollama, LM Studio, llama.cpp out of the box
Cons
- - 16 GB — cannot run 13B+ models
- - 120 GB/s memory bandwidth (lower than Pro/Max chips)
- - No path to memory upgrade after purchase
The Mac mini M4 with 16 GB is the lowest-cost Apple Silicon entry point for local LLMs. It runs 7B models like Llama 3.2 8B and Mistral 7B smoothly at Q4_K_M quantization, delivering 20–30 tokens/second via Ollama or llama.cpp. The 16 GB configuration cannot fit 13B models at Q4 with comfortable headroom — if you need 13B, step up to the 24 GB variant.
Mac mini M4 — 24 GB unified memory
Step-UpMac mini M4 — 24 GB unified memory
M4 · 10-core GPU
Memory
24
Bandwidth
120 GB/s
Max model
13B at Q4_K_M
Speed
15–22 t/s at 13B
Pros
- + Fits 13B models at Q4_K_M comfortably
- + The cheapest 24 GB Apple Silicon option
- + Same silent operation as 16 GB model
Cons
- - 120 GB/s memory bandwidth — same base M4 chip
- - Cannot run 34B models — need M4 Pro for that
- - Slower inference than M4 Pro/Max per token
The 24 GB Mac mini M4 unlocks 13B models at Q4_K_M with roughly 4 GB of headroom for KV cache and system use. It is a meaningful step up from the 16 GB model for a modest price increase, and the right choice if you want Llama 3.1 13B, Mistral Nemo 12B, or Phi-4 (14B) without paying for a Pro chip.
Mac mini M4 Pro — 48 GB unified memory
Mid-Range PowerMac mini M4 Pro — 48 GB unified memory
M4 Pro · 20-core GPU
Memory
48
Bandwidth
273 GB/s
Max model
34B at Q4_K_M
Speed
18–28 t/s at 34B
Pros
- + 273 GB/s memory bandwidth — 2.3x faster than base M4
- + 48 GB fits 34B at Q4_K_M with comfortable headroom
- + Runs 13B at Q8 (near-lossless quality)
- + Still in a small, silent Mac mini form factor
Cons
- - Significant price jump from the 24 GB model
- - Cannot fit 70B — need Mac Studio for that
- - Slower than M4 Max on large models
The Mac mini M4 Pro with 48 GB is a major step forward. The M4 Pro chip more than doubles memory bandwidth to 273 GB/s, and 48 GB fits Llama 3.3 70B at Q4_K_M (~43 GB) — making it the cheapest consumer device that can run 70B models. It costs considerably less than the Mac Studio M4 Max 64GB. Token speed is 10–14 tok/s at 70B, vs 14–20 tok/s on the Mac Studio.
Mac Studio M4 Max — 64 GB unified memory
Best for 70BMac Studio M4 Max — 64 GB unified memory
M4 Max · 40-core GPU
Memory
64
Bandwidth
400 GB/s
Max model
70B at Q4_K_M
Speed
8–15 t/s at 70B
Pros
- + 64 GB fits 70B at Q4_K_M with ~27 GB headroom
- + 400 GB/s memory bandwidth — fastest consumer Apple chip
- + Runs 34B at Q8 (near full precision)
- + Silent, 150 W idle / 300 W peak — far below a GPU PC
- + No driver issues — Metal backend just works
Cons
- - Expensive relative to GPU PCs for smaller models
- - Slower than RTX 4090 on 7B–34B models
- - Tied to macOS ecosystem
The Mac Studio M4 Max with 64 GB is the flagship consumer LLM device for 2026. At 400 GB/s of memory bandwidth and 64 GB of unified memory, it runs Llama 3.3 70B, Qwen3 72B, DeepSeek-R1-Distill-70B, and Llama 4 Scout 109B at Q4_K_M with ~27 GB of headroom to spare. Inference is 8–15 tokens/second on 70B — it runs them at all, which no single consumer GPU can match. For 34B and below, it is fast — 20–40 t/s.
Mac Studio M4 Max — 128 GB unified memory
Server-Class LocalMac Studio M4 Max — 128 GB unified memory
M4 Max · 40-core GPU
Memory
128
Bandwidth
400 GB/s
Max model
70B at FP16 / 100B+ at Q4
Speed
6–12 t/s at 70B Q8
Pros
- + 128 GB — runs 70B at Q8 and FP16 (no consumer GPU can do this)
- + Fits 100B+ models at Q4_K_M
- + Can run two large models simultaneously
- + Same silent, low-power profile as 64 GB model
Cons
- - Server-class price for consumer hardware
- - Same 400 GB/s bandwidth as 64 GB model — not faster per token
- - Overkill for most users — 64 GB covers all common use cases
The 128 GB Mac Studio M4 Max is the only consumer device that can load a 70B model at FP16 (~140 GB) without spilling into swap. At Q4_K_M it fits models in the 100-110B parameter range, which covers Llama 3.1 405B in Q1 (experimental), large Mixtral variants, and multi-model setups. The high price makes sense only for researchers, studios, or anyone running inference as a serious local workload.
Apple Silicon LLM Comparison Table
| Device | Memory | Bandwidth | Price | Max model | Quantization | Speed |
|---|---|---|---|---|---|---|
| Mac mini M4 16GB | 16 GB | 120 GB/s | Check price on Amazon | 7B | Q4_K_M | 20–30 t/s |
| Mac mini M4 24GB | 24 GB | 120 GB/s | Check price on Amazon | 13B | Q4_K_M | 15–22 t/s |
| Mac mini M4 Pro 48GB | 48 GB | 273 GB/s | Check price on Amazon | 70B | Q4_K_M | 10–14 t/s |
| Mac Studio M4 Max 64GB | 64 GB | 400 GB/s | Check price on Amazon | 70B | Q4_K_M | 8–15 t/s |
| Mac Studio M4 Max 128GB | 128 GB | 400 GB/s | Check price on Amazon | 70B FP16 / 100B+ Q4 | FP16 | 6–12 t/s |
Speed figures are approximate tokens/second for the listed max model at the listed quantization. Actual throughput varies with context length, system load, and tool configuration. Use the VRAM Calculator for precise memory requirements. Compare Mac vs GPU options on the Compare page.
Best Tools for Running LLMs on Apple Silicon
All four major local inference tools support Apple Silicon natively. They all use llama.cpp's Metal backend under the hood (or Apple's MLX framework), which directly maps GPU compute to Apple's GPU cores without any translation layer. Here is when to use each one.
Ollama
Recommended for most users
Ollama is the fastest way to get started. One command downloads and runs any model: ollama run llama3.2. It runs as a local server on port 11434, so any OpenAI-compatible client (Open WebUI, Cursor, Continue) can connect to it immediately. Ollama automatically detects Apple Silicon and routes inference through Metal — no configuration needed.
Install: brew install ollama or download from ollama.com
LM Studio
Best GUI experienceLM Studio is a free desktop app with a full graphical interface for browsing, downloading, and chatting with models. It pulls models from Hugging Face directly and shows memory usage, token speed, and GPU utilization in real time. The built-in chat interface is good enough for daily use. It also exposes a local OpenAI-compatible API server. Best choice if you prefer a GUI over the command line.
Download: lmstudio.ai — native Apple Silicon app, no Rosetta
llama.cpp
Best raw performance
llama.cpp is the underlying inference engine that both Ollama and LM Studio use on Apple Silicon. Running it directly gives you the most control: you can tune context size, batch size, GPU layer count, and quantization settings that higher-level tools abstract away. Built with cmake -DGGML_METAL=ON, it routes everything through Metal. For users who want to squeeze every token/second out of their hardware, llama.cpp direct is the right tool.
Build: brew install llama.cpp (prebuilt with Metal) or build from source
MLX
Best for research and fine-tuningMLX is Apple's own machine learning framework, designed from scratch for Apple Silicon. Unlike llama.cpp which is inference-only, MLX supports training and fine-tuning directly on the Mac GPU — useful for LoRA fine-tuning without a cloud GPU. The mlx-lm package provides a clean Python API for text generation. For everyday chat inference, Ollama or llama.cpp will be faster; MLX shines when you need to run or modify model code directly.
Install: pip install mlx-lm — requires Python 3.9+ on Apple Silicon
Apple Silicon vs NVIDIA GPU: Which Should You Choose?
The short answer: buy a GPU PC if you primarily run 7B–34B models and want maximum tokens/second per dollar. Buy a Mac if you want to run 70B models on a single, quiet, low-maintenance device — or if you are already in the macOS ecosystem.
| Criteria | Apple Silicon | NVIDIA GPU PC |
|---|---|---|
| 7B inference speed | 20–30 t/s (M4) | 30–60 t/s (RTX 4090) — faster |
| 34B inference speed | 18–28 t/s (M4 Pro) | 20–40 t/s (RTX 4090) — comparable |
| 70B capability | Yes — 64 GB+ runs 70B at Q4 | Single card: No. Dual RTX 4090: Yes (complex) |
| Power draw | 30–300 W (silent) | 450–575 W GPU alone + system overhead |
| Driver/setup overhead | Near zero — Metal works out of the box | CUDA drivers, occasional updates required |
| Software ecosystem | Ollama, LM Studio, llama.cpp, MLX — all fully supported | CUDA — widest support across all tools incl. fine-tuning |
| Fine-tuning | Possible via MLX — slower than CUDA | Best option — CUDA, bitsandbytes, PEFT all native |
| Hardware for 34B capability | M4 Pro 48GB | RX 7900 XTX or RTX 4090 |
| Hardware for 70B capability | Mac Studio 64GB | Dual RTX 4090 + platform |
For a full side-by-side with any specific hardware combination, use the Compare page. To see exactly how much memory any model needs, use the VRAM Calculator.
Hardware Pages
Frequently Asked Questions
Is a Mac good for running LLMs locally?
Yes — Apple Silicon Macs are excellent for local LLM inference, especially for larger models. The key advantage is unified memory: the CPU, GPU, and Neural Engine share the same memory pool, so a Mac with 64 GB can run a 70B model at Q4_K_M without any PCIe bandwidth bottleneck. An NVIDIA GPU PC is faster for 7B–34B models, but cannot fit 70B on a single card.
What Apple Silicon chip is best for running LLMs in 2026?
The Mac Studio M4 Max (64 GB) is the best Apple Silicon option for most LLM users in 2026. It runs 70B models at Q4_K_M, 34B models at Q8, and handles up to ~100B parameter models at lower quantization. The Mac mini M4 (16 GB) is the best entry point for 7B models, and the M4 Pro (48 GB) covers 34B.
How fast is Apple Silicon for LLM inference?
Apple Silicon inference speed depends on model size and chip. The M4 Max achieves roughly 400 GB/s of memory bandwidth, which translates to 8–15 tokens/second on 70B models at Q4_K_M and 20–40 tokens/second on 13B models. This is slower than an RTX 4090 on 13B models, but the Mac is the only single-device option that fits 70B at all.
What tools can I use to run LLMs on a Mac?
The four main tools are: Ollama (easiest — one command, automatic Metal support), LM Studio (GUI-based, best for beginners), llama.cpp (fastest raw performance, command-line), and MLX (Apple's own framework, best for fine-tuning). All four are free and support Apple Silicon natively.
Can the Mac mini M4 run a 70B model?
No — the Mac mini M4 tops out at 32 GB of unified memory (in the M4 Pro configuration), which is not enough for a 70B model at any standard quantization. 70B at Q4_K_M requires at least 37–40 GB. You need the Mac Studio M4 Max with 64 GB or more.
What is unified memory and why does it matter for LLMs?
Unified memory means the CPU and GPU share the same physical memory pool with no separate VRAM chip. On a traditional PC, moving data to GPU VRAM crosses the PCIe bus at roughly 64 GB/s. Apple Silicon's unified memory runs at 120–800 GB/s and is directly accessible by GPU cores. For LLMs, this means every gigabyte of RAM is usable as effective VRAM at near-GPU speeds.
Is the Mac Studio M4 Max worth it for local LLMs?
If you want to run 70B models locally, the Mac Studio M4 Max (64 GB) is the most practical single-device option. The alternative is dual RTX 4090s (for cards alone, plus a platform), which requires tensor-splitting setup and has much higher power draw. The Mac is also completely silent and requires zero driver management. For users who only need 7B–34B models, a GPU PC is faster and cheaper.
Device-specific guides
Check memory requirements for a specific model, or compare Mac and GPU options side-by-side.
Related Guides
Mac Mini M4 Pro LLM Guide
How the M4 Pro Mac Mini performs for local AI workloads.
Mac vs PC for LLMs
Which platform makes more sense for running local models.
How to Run LLMs Locally
Step-by-step guide to getting your first local model running.
Running 70B Models Locally
Hardware strategies for running the largest open models.
Sources & methodology
VRAM and tokens-per-second figures on this page are synthesised from open community benchmarks. The sitewide formula and the full source list are on the methodology page. For this guide I leaned on:
- Apple MLX. Apple's official inference framework, the reference for Metal-accelerated token rates.
- llama.cpp llama-bench discussion. M-series llama-bench results posted by the llama.cpp maintainers themselves.
- XiongjieDai GPU-Benchmarks-on-LLM-Inference. Side-by-side M1 through M3 Ultra numbers against NVIDIA cards in the same harness.
Spot a number that does not match the linked source? Email [email protected] and I will update the guide.