Apple Mac Mini (M4, 2024)

The M4 chip is built on TSMC’s 2nd-generation 3nm process, featuring a 10-core CPU (4 performance, 6 efficiency) and a 10-core GPU. This generation introduces hardware-accelerated ray tracing to the Mac Mini line, which, while primarily a graphics feature, indicates the increased sophistication of the GPU clusters.

The INT8 performance of 38 TOPS via the 16-core Neural Engine specifically targets "Apple Intelligence" features and CoreML-optimized models. However, most practitioners will utilize the GPU via Metal (using frameworks like llama.cpp or MLX) for broader model compatibility. With a TDP of just 55W, the M4 Mac Mini provides a high-density compute-per-watt ratio, making it an ideal candidate for "always-on" local inference servers or agentic loops that don't justify the power draw of a 300W+ NVIDIA workstation.

The Apple Mac Mini (M4, 2024) local LLM capabilities are defined by its 32GB maximum VRAM ceiling. Because Apple Silicon uses unified memory, the GPU can access nearly the entire 32GB pool (minus a small overhead for the OS), effectively acting as a 32GB GPU for AI. This is a massive advantage over consumer NVIDIA cards like the RTX 4060 (8GB) or 4070 (12GB) which often struggle with model fitting.

The "sweet spot" for this hardware is running 7B at Q4 with 32GB unified memory. At this configuration, the model fits entirely in memory with significant room left for a large KV cache (context window).

• Llama 3.1 8B / Mistral 7B: These models run exceptionally well. At 4-bit (Q4_K_M) or 8-bit (Q8_0) quantization, you can expect high throughput, often exceeding 20–30 tokens per second, which is faster than human reading speed.
• Qwen 2.5 7B / 14B: The 7B variants fly on this hardware. The 14B models can be squeezed in at Q4 quantization, though the 120 GB/s bandwidth will start to show its limits, resulting in slower but usable generation.
• DeepSeek-R1-Distill-Llama-8B: This is a primary use case for the M4 Mini. The reasoning capabilities of DeepSeek's distilled models fit comfortably within the 16GB or 24GB SKUs, making this the best AI chip for local deployment of reasoning agents on a budget.
• Multimodal Models: Vision-language models like LLaVA or Moondream2 run with high responsiveness, making this a solid choice for local image-to-text or visual agent workflows.

For practitioners looking for Apple Mac Mini (M4, 2024) tokens per second benchmarks, the bottleneck will rarely be the 10-core GPU's compute power, but rather the 120 GB/s bandwidth. For the best quality-to-speed tradeoff, stick to Q4_K_M or Q5_K_M quantizations.

The small 5x5 inch form factor and 55W TDP make the M4 Mac Mini the ideal hardware for running persistent local agents. If you are building an agentic workflow using frameworks like LangChain or AutoGPT, this machine can act as a dedicated "brain" that remains powered on 24/7 without significant electricity costs.

Developers building apps on macOS can utilize the M4 to test Apple Intelligence integration and CoreML performance. The front-facing USB-C ports make it easier to swap external storage for large model datasets or connect edge devices for testing.

For those who want to run a "Personal AI" without sending data to the cloud, the 32GB SKU offers enough headroom to run a high-quality 8B model with a 32k+ context window. This is perfect for RAG (Retrieval-Augmented Generation) over personal documents.

The 10Gb Ethernet option makes this a powerful edge node. It can be racked (with third-party mounts) to serve as a compact inference server for local networks, processing video feeds or sensor data via local AI models.

When evaluating Apple Mac Mini (M4, 2024) AI inference performance, it is helpful to look at two main competitors: the previous M2 Pro Mac Mini and a custom PC with an NVIDIA RTX 4060 Ti (16GB).

• Vs. Mac Mini (M2 Pro): The M4 features a faster Neural Engine (38 TOPS vs 15.8 TOPS) and better single-core CPU performance. While the M2 Pro had higher memory bandwidth (200 GB/s), the M4 is more efficient and comes in a much smaller physical footprint at a lower starting price point.
• Vs. RTX 4060 Ti (16GB) Build: A PC build with a 16GB 4060 Ti will generally offer higher raw tokens per second for smaller models due to faster GDDR6 VRAM. However, the Mac Mini’s ability to scale to 32GB of unified memory allows it to run larger models (like 14B or 20B parameters) that simply will not fit on a 16GB GPU without heavy, quality-degrading quantization.
• Vs. Mac Studio (M2 Max): The Mac Studio remains the superior choice for heavy lifting (400 GB/s bandwidth), but for $499–$999, the M4 Mac Mini provides about 60-70% of the utility for a fraction of the cost.

For practitioners who prioritize VRAM capacity and energy efficiency over raw TFLOPS, the M4 Mac Mini is currently the best apple silicon for running AI models locally at the sub-$1,000 price point. Its ability to handle 7B at Q4 with 32GB unified memory makes it a versatile tool for the modern AI engineer's toolkit.