bestllmfor

A good fit for

• Users wanting to run LLMs locally on their own hardware with privacy and no cloud involvement.
• Developers and researchers seeking detailed hardware compatibility and performance benchmarks for LLMs.
• Researchers and developers needing to run very large LLMs (400B+ parameters) locally with high memory capacity.

Know the limitations

• Does not provide LLM models itself but offers guides and rankings; some kits are paid products.
• High-end Mac Studio configurations with large memory are expensive, ranging up to $9,499 for maxed 512GB M3 Ultra.

What you can do

• Running local LLMs on various hardware including RTX GPUs and Apple Silicon Macs.
• Deploying private, local AI assistants and agents without cloud dependency.
• Running frontier-scale open-weight LLMs locally on Mac Studio hardware with large unified memory configurations.
• Using RTX 4080 and 4080 Super GPUs for local LLM inference with 16GB VRAM supporting models up to 20B parameters comfortably.

Features

• Benchmark-driven rankings of LLMs matched to specific hardware configurations.
• Free cost ROI calculator for evaluating LLM deployment costs.
• Open data API providing access to LLM data.
• Detailed hardware memory capacity guidance for selecting Mac Studio configurations based on LLM size requirements.
• Comparative analysis of Apple Silicon chips (M2 Ultra, M3 Ultra, M4 Max) for LLM workloads including CPU/GPU cores, memory bandwidth, and max RAM.

Integrations

Not confirmed yet.

Platforms & data export

• Supports hardware platforms including NVIDIA RTX GPUs, Apple Silicon Macs (M1, M2, M3, M4), and Snapdragon chips.