This guide will walk you step-by-step through installing and configuring our tailored AI environments on your Puget Systems workstation or server.
From Unboxing to Inference: Introducing the Puget Systems Docker App Packs
We look at AI the way our customers do, which is why we built the Puget Systems Docker App Packs: to help you get up and running with AI inference fast!
Standing Up AI Development Quickly for Supercomputing 2025
How I used “Vibe Coding” and 25 years of experience to tame a liquid-cooled supercomputer in two weeks.
Exploring Hybrid CPU/GPU LLM Inference
A brief look into using a hybrid GPU/VRAM + CPU/RAM approach to LLM inference with the KTransformers inference library.
What’s the deal with NPUs?
An introduction to NPU hardware and its growing presence outside of mobile computing devices.
Local alternatives to Cloud AI services
Presenting local AI-powered software options for tasks such as image & text generation, automatic speech recognition, and frame interpolation.
AMD Zen4 Threadripper PRO vs Intel Xeon-w9 For Science and Engineering
The performance improvement with the new Zen4 TrPRO over the Zen3 TrPRO is very impressive!
My first recommendation for a Scientific and Engineering workstation CPU would now be the AMD Zen4 architecture as either Zen4 Threadripper PRO or Zen4 EPYC for multi-socket systems.
Benchmarking with TensorRT-LLM
Evaluating the speed of GeForce RTX 40-Series GPUs using NVIDIA’s TensorRT-LLM tool for benchmarking GPU inference performance.
Experiences with Multi-GPU Stable Diffusion Training
Results and thoughts with regard to testing a variety of Stable Diffusion training methods using multiple GPUs.
LLM Server Setup Part 2 — Container Tools
This post is Part 2 in a series on how to configure a system for LLM deployments and development usage. Part 2 is about installing and configuring container tools, Docker and NVIDIA Enroot.
