# MLX Unified Memory vs eGPU GDDR7 Hardware Architecture > Source-anchored research for MLX Unified Memory vs eGPU GDDR7 Hardware Architecture. Run status: COMPLETED. Parent: [Local Model Performance Evaluation: Ollama, MLX, and Hardware Architectures](https://llms-explorer.com/tree/local-model-performance-evaluation/) · 4 facets · 4 facts · page: https://llms-explorer.com/tree/mlx-unified-memory-vs-egpu-gddr7-hardware-architecture/ ## Structure and components - Attempting to run models larger than 16GB across a Thunderbolt eGPU requires layer-by-layer weight streaming that collapses token generation throughput to sub-1 token per second. (high confidence) — [source](https://github.com/tinygrad/tinygrad) ## Problems, failure modes and limitations - Discrete eGPU GDDR7 achieves ~1000 GB/s memory bandwidth, delivering over 2x the memory-bound token generation throughput of Apple Silicon unified memory for models that fit within its 16GB VRAM limit. (high confidence) — [source](https://github.com/tinygrad/tinygrad) ## Comparisons and alternatives - Thunderbolt 5 eGPU links provide approximately 7.0 GB/s practical unidirectional bandwidth, creating a severe bottleneck if model weights are streamed from host RAM rather than fully resident in GDDR7 VRAM. (high confidence) — [source](https://github.com/tinygrad/tinygrad) ## Facts and statements - Apple Silicon Unified Memory provides a shared address space allowing CPU and Metal GPU to operate on identical memory pointers without PCIe serialization or host-device DMA copies. (high confidence) — [source](https://github.com/ml-explore/mlx)