Apple Mac Mini (M2, 2023)

Memory Bandwidth and Throughput

The M2 provides 100 GB/s of memory bandwidth. While this is lower than the M2 Pro (200 GB/s) or the M2 Ultra (800 GB/s), it provides sufficient throughput for real-time text generation on smaller models. For context, 100 GB/s is roughly triple the bandwidth of standard DDR5 desktop memory, which is why Apple Silicon consistently outperforms similarly priced x86 desktops without dedicated GPUs in LLM inference tasks.

• GPU Cores: 10 cores capable of hardware-accelerated matrix multiplication via the Metal Performance Shaders (MPS) backend.
• Neural Engine: A 16-core NPU designed specifically for CoreML tasks, though most LLM practitioners will utilize the GPU via llama.cpp or MLX.
• Power Efficiency: With a TDP of only 39W, the M2 Mac Mini is one of the most energy-efficient AI deployment options available. It can run at full inference load for hours without thermal throttling, making it an ideal candidate for "always-on" local AI agents.

The Apple Mac Mini (M2, 2023) AI inference performance is optimized for models in the 3B to 8B parameter range. Because the system shares memory with the OS, a 24GB configuration typically leaves approximately 18–20GB available for the model weights and KV cache.

• Llama 3.1 8B: This is the "sweet spot" for the M2. At 4-bit quantization (Q4_K_M), the model uses roughly 5–6GB of VRAM, leaving plenty of overhead for long context windows. You can expect speeds of 15–20 tokens per second, which is faster than the average human reading speed.
• Mistral 7B / Nemo 12B: These models run comfortably at Q5 or Q6 quantization. The 12B models push the bandwidth harder but remain highly usable for agentic tasks.
• DeepSeek-R1-Distill-Llama-8B: The M2 handles the distilled reasoning models with ease, providing a local "thinking" agent capable of complex logic within a compact power envelope.
• Qwen 2.5 7B / 14B: The 7B variant runs at peak performance, while the 14B variant requires Q4 quantization to maintain a fluid experience.

The M2’s ProRes hardware acceleration and 10-core GPU make it surprisingly capable for vision-language models (VLMs) like LLaVA 1.6 or Moondream2. It can handle image-to-text descriptions and local OCR tasks with sub-second latency, making it a viable hub for local vision-based automation.

For the best quality-to-speed tradeoff on the M2, practitioners should aim for Q4_K_M or Q5_K_M GGUF format. While the 24GB VRAM allows for 7B models at Q8 (near-lossless), the marginal gains in accuracy are often outweighed by the slight dip in tokens per second compared to Q5.

The M2 Mac Mini is perhaps the best hardware for local AI agents in 2025 for those on a budget. Because it is silent and low-power, it can act as a "Home AI Server" running Ollama or vLLM in the background, serving requests to other devices on the network via an API.

For developers building AI-powered applications, the M2 provides a native environment to test CoreML and MLX implementations. Apple’s MLX framework, specifically designed for Apple Silicon, allows the M2 to perform fine-tuning on small datasets (LoRA) for 3B and 7B models, which is generally not feasible on consumer laptops with 8GB or 16GB of RAM.

Due to its 2.6 lb weight and small footprint (7.7 inches square), the M2 Mac Mini is frequently used in edge deployment scenarios—such as retail analytics or on-site data processing—where a full server rack is unavailable but high-reliability inference is required.

When evaluating the Apple Mac Mini (M2, 2023) for AI, it is most often compared to DIY PC builds or the newer M3/M4 iterations.

A budget PC with an RTX 3060 12GB offers faster raw inference for models that fit within its 12GB VRAM. However, the M2 Mac Mini (24GB) can run significantly larger models (up to 14B or 20B parameters) that the 3060 simply cannot load without falling back to slow system RAM. Additionally, the Mac Mini consumes roughly 1/5th the power of a dedicated GPU desktop.

The M2 Pro variant doubles the memory bandwidth to 200 GB/s. If your workload requires high-speed token generation for multiple concurrent users, the M2 Pro is the superior choice. However, for a single user or an autonomous agent, the base M2 with 24GB of unified memory is a more cost-effective way to get the necessary VRAM for large language models.

The M4 Mac Mini offers a smaller footprint and faster per-core performance. However, for AI practitioners, the primary constraint is memory capacity and bandwidth. If you can find an M2 Mac Mini with 24GB of memory at a discount, it remains a highly competitive "24GB GPU for AI" compared to buying a newer base-model M4 with limited RAM.