Dual AMD Radeon AI PRO R9700 Workstation Beats GPT-5.6 Sol on Cost After 3.5 Hours Weekly, Review Says

The evaluation measured electricity draw, token throughput and amortized hardware cost, then compared those figures with cloud subscription tiers. Cloud providers charge per million tokens: GPT-5.6 Luna at $1.20, Claude Sonnet 5 at $10, Claude Opus 5 at $25, and GPT-5.6 Sol at $30, according to the report. The more expensive the cloud model a team would otherwise use, the sooner owned hardware pays for itself.

In the test, both AMD cards together generated 156.2 tokens per second while serving eight simultaneous users. A technique called Multi-Token Prediction nearly doubled throughput to 320.2 tokens per second with identical output quality. At that speed, the machine needs only 3.5 hours of weekly use to beat GPT-5.6 Sol on cost, 4.2 hours to beat Claude Opus 5, and 8.8 hours to beat Gemini 3.1 Pro.

For a team generating above 20 million tokens per month against GPT-5.6 Sol pricing, the savings become material. At that volume, running the workstation costs about $6,262 yearly in electricity and amortized hardware, against roughly $18,000 yearly in matching Sol fees. That $11,738 yearly gap is the evidence behind the claim that heavy monthly usage makes AMD's rig worthwhile, TechRadar reported.

The math reverses if a company relies on GPT-5.6 Luna at $1.20 per million tokens. The workstation would then need 94.3 hours of weekly use just to match that cheaper cloud subscription's total cost. Since a week has 168 hours, reaching that break-even point is difficult without near-constant, saturated usage. Electricity was a minor factor: both cards together drew between 310 and 510 watts under sustained load.

Usage volume also determines whether a smaller deployment makes sense. A smaller team producing only five million tokens monthly, priced against Gemini 3.1 Pro, would spend $6,262 yearly to displace just $720 in cloud costs, a clear loss. That comparison shows the hardware only makes financial sense once usage climbs high enough to close a large yearly cost gap.

A single R9700 card handled an eight billion parameter AI model alone, processing 34.5 tokens per second without a second card. Running one card also lowers the effective break-even point because it draws far less power under equivalent load. The single card pulled between 221 and 283 watts depending on simultaneous user count, well under the 310 to 510 watts both cards drew together. Smaller models therefore offer a second path into positive economics, letting lighter workloads justify a $1,880 single-card purchase before a team can justify the full $3,760 dual-card upgrade.

Price alone does not settle the question. Locally run models measurably trail top cloud systems on complex reasoning benchmarks, according to the report. On one independent intelligence index, the 27 billion parameter AMD model scored 37 points against Google's Gemini 3.1 Pro scoring 46 points. A team chasing the cheaper token count may be trading away real reasoning quality, not just cloud subscription fees. The review was published via Puget Systems, TechRadar said.