Apple Silicon Macs have become among the most capable consumer hardware for local AI inference. The unified memory architecture — where CPU and GPU share the same high-bandwidth memory pool — eliminates the VRAM bottleneck that limits discrete GPU setups and allows running much larger models than the equivalent NVIDIA card. If you are choosing a Mac for local LLM work, or evaluating whether an upgrade is worth it, this guide covers the real numbers and practical implications of each chip.
Why Apple Silicon Is Excellent for Local LLMs
The key advantage is unified memory. A MacBook Pro M4 Max with 128GB unified memory can run a 70B parameter model (requiring ~40GB in Q4_K_M quantization) at full speed because all 128GB is accessible to the GPU at high bandwidth — roughly 400-800 GB/s on M4 Pro and Max chips. An NVIDIA RTX 4090 has 24GB VRAM — models larger than ~14B at Q4 quantization require spilling to system RAM over a slow PCIe connection, dramatically reducing speed. For large model inference, Apple Silicon’s memory architecture is architecturally superior to most consumer NVIDIA setups.
The second advantage is efficiency. Apple Silicon inference consumes 15-30 watts during active LLM generation versus 200-400 watts for a high-end NVIDIA GPU under load. For always-on or frequently used local AI, this difference adds up to meaningful electricity savings and significantly lower thermal output — important in home and office environments where noise and heat from GPU cooling fans are concerns.
M4 Pro: The Everyday AI Mac
The M4 Pro is the entry point for serious local AI work on Apple Silicon. Available in MacBook Pro 14 and 16-inch, Mac mini, and iMac configurations. The key specs relevant to LLM inference:
Memory bandwidth: 273 GB/s (12-core) to 300 GB/s (16-core). This is the primary determinant of tokens-per-second speed for LLM inference — higher bandwidth means faster generation.
Unified memory options: 24GB, 48GB. The 24GB configuration runs 7B models comfortably and 13B-14B models at reduced quantization. The 48GB configuration comfortably handles 14B models and can run 30B+ models at aggressive quantization.
Typical eval rates with Ollama: 7B Q4_K_M model at 55-75 tokens/second. 14B Q4_K_M at 28-40 t/s. 34B Q4_K_M (fits in 48GB) at 12-18 t/s.
The M4 Pro is the right choice for developers and users who primarily work with 7B-14B models and want a capable laptop or compact desktop. The 24GB base configuration handles the majority of practical local AI use cases. The 48GB option makes sense if you regularly work with 30B+ models or want headroom for running multiple models simultaneously.
M4 Max: The Power User’s Choice
The M4 Max offers substantially more memory bandwidth and higher maximum unified memory than the M4 Pro. Available in MacBook Pro 16-inch, Mac Studio, and Mac mini configurations.
Memory bandwidth: 400-546 GB/s depending on configuration. This is 45-80% more bandwidth than the M4 Pro — a direct multiplier on inference speed.
Unified memory options: 36GB, 64GB, 128GB. The 128GB configuration can run a 70B Q4_K_M model (~40GB) at full GPU speed with significant headroom remaining for context and system memory. This is effectively impossible on any single consumer NVIDIA card.
Typical eval rates with Ollama: 7B Q4_K_M at 90-120 t/s. 14B Q4_K_M at 50-70 t/s. 70B Q4_K_M (in 128GB config) at 18-28 t/s.
The M4 Max is the right choice when you regularly work with 30B+ models, want fast interactive inference at 70B scale, or need the raw memory bandwidth for faster 7B-14B generation. The bandwidth advantage over M4 Pro is tangible in daily use — responses appear noticeably faster at equivalent model sizes.
Figure 1 — Apple Silicon LLM Performance Comparison (Ollama, Q4_K_M quantization)
M4 Ultra: The Workstation-Class Option
The M4 Ultra is two M4 Max dies connected via Apple’s UltraFusion interconnect, effectively doubling the GPU cores, memory bandwidth, and unified memory capacity. Available only in Mac Studio and Mac Pro.
Memory bandwidth: 800 GB/s — the highest of any Apple Silicon chip and comparable to server-class hardware.
Unified memory options: 192GB and 512GB. The 192GB configuration comfortably runs 70B models with fast inference and can fit 100B+ models at aggressive quantization. The 512GB configuration can run models that are otherwise impossible on consumer hardware — 405B models like Llama 3.1 405B Q4 (~240GB), which produces outputs approaching the quality of frontier cloud models.
Typical eval rates: 7B at 140-180 t/s. 14B at 85-110 t/s. 70B at 35-50 t/s. 405B at 8-12 t/s (slow but functional on hardware where it is otherwise impossible).
The M4 Ultra is the right choice for teams running AI as a service for multiple concurrent users, researchers who need access to the largest open-weights models, and production deployments where throughput and model quality both matter. The price premium over M4 Max is significant — the Mac Studio Ultra starts at roughly 3x the price of a comparable MacBook Pro M4 Max — but the capability jump (particularly 192-512GB unified memory) is equally significant for large model work.
How to Choose: A Decision Framework
The practical decision usually comes down to two questions: which models do you need to run, and what is your budget? For models up to 7B, any M4 chip including the base M4 (not covered here, found in MacBook Air and base MacBook Pro) provides excellent performance. For 7B-14B models as your primary use, M4 Pro 24GB is the sweet spot — good performance, reasonable cost, laptop portability available. For 14B-34B models regularly, M4 Pro 48GB or M4 Max 64GB. For 70B models at interactive speed, M4 Max 128GB is the minimum — 70B at 20+ t/s on M4 Pro 48GB is possible but slow. For 70B+ at production throughput or 100B+ models, M4 Ultra 192GB. For the absolute largest open-weights models (405B), M4 Ultra 512GB is the only consumer option.
Memory Configuration Advice
Unified memory cannot be upgraded after purchase — this is the most important buying decision for Apple Silicon AI hardware. The rule of thumb: buy more memory than you think you need today, because model sizes increase over time and the ability to run newer, larger models on existing hardware is valuable. A 24GB M4 Pro bought today will be unable to run the frontier open-weights models of 2027-2028, which will almost certainly be larger than current 70B models. A 128GB M4 Max or 192GB M4 Ultra gives substantially more runway. If your budget allows only 24GB, that is still excellent hardware for today’s 7B-14B models — just be aware that it will be the binding constraint as models grow. The memory bandwidth also scales with the memory configuration in Apple Silicon (more memory = higher bandwidth chip tier), so the performance gains from more memory are compounded by the bandwidth improvement.
Flash Attention on Apple Silicon
Enable Flash Attention for all Apple Silicon inference — it is particularly impactful on M-series chips due to the memory bandwidth architecture:
export OLLAMA_FLASH_ATTENTION=1 # add to ~/.zshrc
# Restart Ollama after settingOn M4 Max and Ultra, Flash Attention reduces KV cache memory usage by 30-50% at long context lengths, which directly translates to either fitting larger models or using longer context windows on the same hardware. The speed improvement at 8K+ context lengths is 15-30%. There is no downside to enabling it on Apple Silicon — it should always be on.
Comparing to NVIDIA: When to Choose Each
Apple Silicon is not always the right choice for local AI. If you already have a high-end NVIDIA GPU (RTX 4090, RTX 5090), it likely outperforms M4 Pro on 7B-14B models in raw tokens per second — a 4090 generates 7B models at 80-110 t/s versus 55-75 for M4 Pro. The Apple Silicon advantage is specifically in large models: for anything above 24B parameters, unified memory allows Apple Silicon to run models that would require expensive multi-GPU NVIDIA setups. For users who primarily work with 7B-14B models and have a capable NVIDIA GPU, adding a Mac is not necessary. For users who want to run 34B, 70B, or larger models regularly, Apple Silicon provides the most accessible hardware path to do so. The NVIDIA ecosystem also has stronger support for fine-tuning workloads (the transformers and CUDA training ecosystem is mature), while Apple Silicon is primarily strong on inference. If your work is inference-only (running models, not training them), the Apple Silicon hardware is excellent. If you need to do fine-tuning, CUDA-based hardware (NVIDIA) has a broader and more mature tooling ecosystem.