This analysis evaluates memory requirements for running local AI models and generative video workloads on Apple Silicon Macs. Testing indicates that while 32GB is sufficient for general experimentation, generative video pipelines like LTX-2.3 can exceed 115GB of system memory during decoding.
The shift toward local AI execution reduces reliance on cloud-based inference costs but places unprecedented demands on production hardware. Apple's unified memory architecture provides a distinct advantage for streaming professionals by allowing large generative models to share a single high-capacity pool, bypassing the VRAM limitations of traditional discrete graphics cards. As generative video becomes a standard part of the creative workflow, the 128GB threshold will likely transition from a niche requirement to a baseline for high-resolution local rendering. Watch for whether future Apple Silicon iterations prioritize memory bandwidth or raw capacity to handle the 100GB+ spikes seen during video decoding.
Recent testing shows that generative video workloads, such as LTX-2.3, can consume over 115GB of system memory during VAE decoding. Apple's unified memory architecture allows high-capacity Mac configurations to run large-parameter models that exceed the VRAM limits of discrete GPUs, making 128GB a critical threshold for professional local AI workflows.
Generative video pipelines like LTX-2.3 can peak at 115.16GB of memory usage during VAE decoding stages, making 128GB configurations necessary for high-resolution local rendering.
Unified memory allows Apple Silicon to share a single high-capacity pool, enabling the execution of large models like GPT-OSS 120B that cannot fit into the 32GB VRAM capacity of discrete graphics cards like the Nvidia RTX 5090.
Llama 3.3 70B models require at least 48GB of unified memory, though 64GB is recommended to ensure stable performance.
Yes. Increasing context windows from 2K to 64K on 30B-class models raises memory consumption from 18.11GB to 24.26GB while also significantly slowing down token processing speeds.
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