Dynamic Layer Tiering

How Alfred Linux seamlessly bridges GPU VRAM and CPU RAM to run massive, 70B+ parameter AI models locally without out-of-memory crashes.

Dynamic Layer Tiering Visualization

The Hardware Handshake

Because Alfred Linux meticulously bakes the raw NVIDIA open-gpu-kernel-modules and AMD's ROCm stacks into the offline master vault, the OS possesses bare-metal access to the GPU's CUDA/HIP cores the literal millisecond it boots.

When you pull a massive model offline, the backend engine instantly interrogates the PCI bus to determine exactly how much VRAM is available. There is zero manual configuration.

Dynamic Layer Splitting

Neural networks are built in mathematical "layers." If you load a 40GB model, but your RTX 4090 only has 24GB of VRAM, a standard operating system will immediately crash with a fatal "Out of Memory" error.

Alfred doesn't crash. Instead, the AI engine physically slices the neural network based on the exact hardware detected on the PCIe bus.

The Splitting Architecture
  • VRAM Packing: It crams the maximum number of layers it possibly can (e.g., 30 out of 40 layers) into the hyper-fast GPU VRAM.
  • RAM Overflow: It gracefully dumps the remaining layers (e.g., the last 10 layers) into standard System RAM.

The Overflow Execution

When you interrogate the AI, the data shoots through the GPU at lightspeed for the first layers, and then seamlessly overflows to the CPU to finish processing the remainder.

The Result: The model runs perfectly. It guarantees that the AI never crashes and the user can run models vastly larger than their hardware technically supports. You just type ollama run llama3:70b, and Alfred negotiates the entire VRAM/RAM split automatically behind the scenes.