Hardware Matrices for Local LLMs

Restricted to small models (7B-13B) via aggressive CPU offloading due to zero dedicated VRAM. Thermal constraints in this chassis will throttle sustained inference performance.

Fails the absolute binary rejection rule for Mini PC form factors. While the 32GB RAM is adequate for basic offloading, the lack of a dedicated GPU and compact design make it unsuitable for serious local AI workloads.

With 64GB of system RAM, this workstation excels at CPU offloading, comfortably running 7B-13B models and enabling basic 30B inference. Best suited for local AI learning, prompt engineering, and multi-agent workflows where VRAM isn't critical.

Priced at $727, it delivers exceptional value for CPU-bound inference and development tasks. The tower chassis provides robust cooling and easy upgrade paths for future GPU additions.

Optimized for CPU offloading of 7B-13B parameter models, making it a solid entry point for Ollama and lightweight agent workflows. Inference speeds will be modest, but the 32GB DDR5 pool allows comfortable quantized model loading.

Delivers a rare 32GB DDR5 configuration at a highly competitive price, removing the biggest bottleneck for local AI experimentation. The Ryzen 7 8745HS offers strong multi-core throughput for CPU-based inference, ideal for learning and prompt tuning.

Relies entirely on CPU offloading due to integrated graphics; best suited for testing 7B-13B parameter models or experimenting with 30B+ models at low quantization, though inference speeds will be notably slow.

The generous 64GB RAM capacity provides a rare, budget-friendly platform for CPU offloading experiments and local AI learning, bypassing the need for expensive dedicated GPUs.

Strong capacity for CPU offloading 7B-13B quantized models, but lacks dedicated VRAM for fast inference or larger 30B+ parameter runs.

Offers a high RAM ceiling at a competitive price, making it a practical entry point for local LLM experimentation without a dedicated GPU.

Relies entirely on CPU offloading due to missing GPU, limiting practical inference to small 3B-7B models with slow token generation. The 64GB RAM provides ample capacity for context windows, but the 2016-era Xeons lack modern AI instruction sets.

Offers an unusually low price for a dual-socket workstation with 64GB RAM and eight PCIe slots, ideal for budget builders planning to add modern GPUs later. Not recommended for immediate local LLM deployment due to outdated CPU architecture.

Excellent for CPU offloading with 64GB RAM, capable of running 13B-34B parameter models smoothly via system memory, though token generation will be noticeably slower than dedicated GPU setups.

Stands out for its massive 64GB RAM at a competitive price, making it a highly cost-effective learning sandbox for experimenting with larger local models without the premium cost of discrete graphics.

High 64GB system RAM enables viable CPU offloading for 7B-13B parameter models and lightweight multi-agent workflows, though token generation will be significantly slower than dedicated GPU setups.

Delivers exceptional memory capacity for a low-budget build, making it a practical choice for developers who need a high-RAM sandbox for local LLM testing and fine-tuning experiments.