Z16 mainframe CPU modules use a Samsung 7 nanometer process technology for chip fabrication.

When running Llama 3.1 on the RTX 4080, GPU utilization averaged around 75% with spikes to 100%, consuming 16GB of host system memory.

The Mac Pro with M2 Ultra chip and 128GB unified memory architecture (where all RAM is available as VRAM) produces very rapid inference responses for Llama 3.1, with GPU utilization spiking around 50%, making it a highly performant platform for local LLM inference.

A 4-year-old Thread Ripper 3970x CPU with 32 cores and 128GB RAM paired with an Nvidia RTX 4080 GPU serves as a reasonable functional equivalent to a contemporary gaming PC despite its age.

Running Llama 3.2 on the high-end workstation produces inference responses at real-time speed that are fast enough to be 'incredibly quick' and 'Speedy', making the model practical for deployment.

The Hertz Mini PC (under $400 base model) running Ollama with Llama 3.1 produces responses at approximately the same speed as ChatGPT, making it the best price-performance value for local LLM inference among tested hardware.

The Hertz Mini PC's AMD Radeon 780M integrated GPU with 6GB of dedicated memory cannot run the full Llama 3.1 (70 billion parameter) model due to insufficient VRAM, forcing the system to use CPU inference instead, despite being marketed as an 'AI Mini PC'.

The Nvidia RTX 4080 GPU in a consumer gaming PC can run Llama 3.1 (70 billion parameters) at approximately ChatGPT speed with 16GB of host memory usage and averaging 75% GPU utilization with spikes to 100%, making this configuration faster than ChatGPT for local inference.

The Raspberry Pi CPU peaks at 84°C while running full-speed inference on all four cores at 100% utilization, using approximately 6GB of the available 8GB RAM.

The high-end workstation configuration tested features an overclocked 96-core Thread Ripper CPU set to run at more than 800 watts TDP, consuming more than 1,200 watts total at the wall when combined with the Nvidia RTX 6000 Ada GPU.

The Hertz Mini PC features a Ryzen 9 7940 HS processor with 4 GHz base clock and 5.2 GHz boost clock, and includes an AMD Radeon 780M RDNA3 iGPU running at 2800 MHz.

The 405 billion parameter Llama 3.1 model produces only one token every several seconds of real wall-clock time on a high-end workstation, making response generation effectively unusable for interactive dialogue.

The Thread Ripper workstation with 512GB of RAM has sufficient memory to load the full 405 billion parameter Llama 3.1 model, with peak memory usage reaching close to 200GB.

The Llama 3.1 405 billion parameter model requires 228GB of download storage and approximately 200GB of RAM to load, but produces only one token every several seconds on a $50,000 Dell workstation with 512GB RAM and dual overclocked Thread Ripper 96-core CPU with RTX 6000 Ada GPU, taking approximately 30 minutes to generate a simple response.

Model size and computational complexity have nearly equal or greater impact on LLM inference performance than the underlying hardware, meaning a poorly-sized model can neutralize the advantage of expensive, powerful machines.

Ollama can be installed directly on Windows without requiring Linux or WSL2, providing a simpler installation path for Windows users wanting to run local LLMs.

WSL2 (Windows Subsystem for Linux 2) supports GPU passthrough, allowing Windows users to run Linux-based tools like Ollama with direct GPU acceleration without full virtualization overhead.

On a Raspberry Pi running Llama inference, the most active system process is Ollama itself, confirmed by task monitoring tools.

The Hertz Mini PC uses integrated LPDDR5 SODIMM memory with a vapor chamber cooler for the CPU, can reduce power draw to 90 watts, and is equipped with a 140-watt external power supply.

The Ollama model manifest verification process is time-consuming and requires checking the SHA digest of large model files (5GB+) before they become usable.