Best AI Workstations

Enterprise buyers who need configurations the major OEMs can't match are the audience for the Puget Systems Genesis II, a professionally built and deeply customizable workstation. Specs run to AMD Threadripper Pro, up to 4x RTX 4090 (or RTX Ada workstation cards), and 256 GB ECC DDR4, and Puget's hand-tuned assembly shows in both performance and build quality, with a quiet edition among the options. On local LLM work, a single RTX 4090 (24 GB VRAM at 1008 GB/s) runs 70B Q4 with system-RAM offload at roughly 10-20 tokens/sec; a 4x RTX 4090 configuration pools 96 GB of VRAM, holds 70B Q4 entirely in VRAM, and reaches 30-40 tokens/sec through tensor parallelism. The steep entry price limits it to buyers who can justify a $10K+ workstation. If local LLMs are the only goal, the DGX Spark below offers comparable model-size headroom at less than half the price.

HP's Z6 G5 A is the smallest Threadripper Pro OEM workstation on the market and the rational mid-tier pick below the flagship Z8 Fury G5. Reviewers at PCMag, AnandTech, StorageReview, Phoronix, and DEVELOP3D consistently praised the build quality, toolless serviceability, and 96-core CPU ceiling, with StorageReview giving it their 'highest recommendation for a high-end tower workstation.' For local-LLM use, configurations with 1-3 RTX 6000 Ada GPUs (48 GB VRAM each at ~960 GB/s) land in the 25-40 tokens/sec range on Llama-3-70B Q4 with a single GPU, and go substantially higher with multi-GPU tensor parallelism. One caveat: none of the published professional reviews ran formal Llama-3 70B Q4 benchmarks, so the LLM-specific numbers here come from single-GPU norms rather than measurements on the HP Z6 itself.

The NVIDIA DGX Spark is the productized version of Project DIGITS — a 150 x 150 x 50.5 mm box housing the GB10 Grace Blackwell Superchip, 128 GB unified LPDDR5x, and the full CUDA AI stack out of the box. Tom's Hardware called it 'a well-rounded toolkit for local AI'; ServeTheHome called it 'must-have for AI developers'; LMSYS published the most thorough independent benchmarks. The 128 GB unified-memory ceiling is the headline feature: it loads models that would otherwise need a $30K+ multi-GPU rig. The catch is bandwidth-limited decode — LMSYS measured Llama-3.1 70B FP8 at 2.7 tokens/sec single-batch, while GPT-OSS 120B (MoE, ~17B active) hits ~14.5 tokens/sec per ServeTheHome. Best understood as a CUDA-native development box for buyers who need to iterate on big-model code without renting cloud GPUs.

HP's flagship workstation, the Z8 Fury G5, is a formidable and highly scalable tower aimed at demanding professional media, VFX, and AI creators who need a four-GPU ceiling. Built around Intel's Xeon W9-3495X (56 cores), 128 GB DDR5 ECC, and up to four NVIDIA RTX A6000 cards, it is a credible local-LLM training and inference rig at the upper end. Configured prices vary enormously: a 1-GPU base build lands around $7,995, a 2-GPU build around $14,000, and a fully loaded 4x RTX A6000 configuration pushes well past $25,000. The price field below reflects a typical 1-GPU configured build, so readers planning multi-GPU AI work should expect to roughly triple that figure.