MacBook Pro 14-inch M4 Max (2024)

The hardware profile of the MacBook Pro 14-inch M4 Max (2024) is defined by its memory architecture. In AI inference, the primary bottleneck is almost always memory bandwidth, not raw compute. The M4 Max addresses this with a 546 GB/s memory bandwidth, a figure that rivals entry-level data center hardware and significantly outperforms the 100-200 GB/s found in standard consumer laptops.

• Unified Memory (VRAM): Up to 128GB LPDDR5X. Because this memory is shared between the CPU and GPU, the M4 Max provides a 128GB GPU for AI workloads (minus a small overhead for the OS), enabling the execution of massive models.
• Compute: 16-core CPU and a 40-core GPU.
• Neural Engine: 16-core dedicated accelerator delivering 38 TOPS (INT8), optimized for CoreML and Apple’s MLX framework.
• Memory Bandwidth: 546 GB/s. This is the "speed limit" for how fast the GPU can access model weights, directly dictating tokens per second.
• Power Efficiency: With a 92W TDP, the M4 Max maintains high inference speeds even on battery power, a feat unattainable by Windows-based competitors that throttle performance significantly when unplugged.

When evaluating MacBook Pro 14-inch M4 Max (2024) AI inference performance, the integration of the MLX framework is vital. MLX allows the GPU to utilize the unified memory architecture efficiently, providing a performance profile that makes the 14-inch M4 Max the best AI chip for local deployment in a mobile form factor.

The standout feature of this machine is its ability to handle hardware for running ~200B parameter LLMs with 128GB unified parameter models. While a 16GB GPU is limited to 7B or 8B parameter models at high precision, the 128GB M4 Max opens the door to the industry's most capable open-weight models.

• Llama 3.1 405B: While the full 405B model is too large, the 14-inch M4 Max can run highly quantized versions (e.g., 2-bit or 3-bit) for research purposes, though at lower tokens per second.
• Llama 3.1 70B / 80B: This is the "sweet spot." At 4-bit (Q4_K_M) or 8-bit quantization, these models run with high fluidity, making them viable for daily development tasks.
• DeepSeek-V3 / DeepSeek-R1: The 128GB capacity allows for running these MoE (Mixture of Experts) models locally, providing reasoning capabilities previously reserved for API calls.
• Qwen 2.5 72B & Mixtral 8x22B: These models fit comfortably within the 128GB VRAM buffer with room to spare for massive context windows (up to 128k tokens).

Based on the 546 GB/s bandwidth, the MacBook Pro 14-inch M4 Max (2024) tokens per second typically range as follows:

• Llama 3.1 8B (FP16): ~60-80 tokens/sec.
• Llama 3.1 70B (Quantized Q4_0): ~8-12 tokens/sec (highly usable for interactive chat).
• Mistral Large 2 / Mixtral 8x22B: ~6-10 tokens/sec.

For multimodal models like LLaVA or CogVLM, the 40-core GPU handles image encoding and text generation with negligible latency, making it an ideal platform for vision-language research.

The MacBook Pro 14-inch M4 Max (2024) is not a general-purpose consumer laptop; it is a specialized tool for AI development.

• Local AI Agent Developers: For those building best hardware for local AI agents 2025, the 128GB memory is essential. Running an agentic workflow often requires keeping an LLM, a vector database, and an embedding model in memory simultaneously. The M4 Max handles this without swapping to the SSD.
• ML Engineers and Researchers: It serves as a "local sandbox." Instead of burning cloud credits to debug a training script or test a prompt's efficacy on a large model, engineers can run the full stack locally.
• Privacy-Centric Organizations: For teams working with sensitive data that cannot leave the local machine, the M4 Max provides the only mobile path to running high-parameter models (70B+) with acceptable performance.
• Long-Context Tasks: With 128GB VRAM for large language models, users can allocate 32GB or even 64GB specifically to the KV cache, enabling the processing of entire codebases or long legal documents in a single prompt.

When evaluating the MacBook Pro 14-inch M4 Max (2024) vs. competitors, the landscape is divided between raw compute power and memory capacity.

The upcoming RTX 5090 mobile (and the current 4090 mobile) offers higher raw TFLOPS, which can result in faster processing for small models (sub-20B). However, NVIDIA mobile chips are capped at 16GB of VRAM. If your workload involves models larger than 20B parameters, the M4 Max is the objective winner because it can actually load the weights into memory, whereas the NVIDIA laptop will be forced to use system RAM (GTT), slowing inference to a crawl (1-2 tokens/sec).

The 14-inch and 16-inch models share the same M4 Max chip and 128GB memory ceiling. The 14-inch is the preferred choice for practitioners prioritizing mobility and "edge" development. However, the 16-inch model has a larger thermal envelope, which may result in slightly less fan noise during sustained, multi-hour inference sessions. For most AI workloads—which are bursty in nature—the 14-inch model provides identical performance in a much more portable 3.4 lbs frame.

While a desktop with an RTX 6000 Ada (48GB VRAM) will offer faster token generation, a single card still cannot match the 128GB capacity of the M4 Max. To exceed the M4 Max's memory capacity in a PC, you would need a dual-GPU setup (e.g., 2x RTX 3090/4090), which consumes over 800W of power and requires a dedicated desktop chassis. The M4 Max achieves its results at a fraction of the power (92W), making it the most efficient Apple AI PC for AI development.