Turnkey and validated
Engineered by AMD, Spectro Cloud, and Supermicro
70% cost savings
vs. frontier-only, with payback in about 6 months
Up to 95%
of frontier model performance with the latest open local models
50 developers per node*
with 30 concurrent users on one Supermicro server
*TCO and payback figures modeled on a 50-developer scenario with mixed local and frontier usage. Actual outcomes depend on utilization, model mix, and frontier model pricing. Visit www.spectrocloud.com/ai-tco to calculate your cost savings
Get the inside scoop on local inference
At AMD's Advancing AI event, we sat down with AMD, Supermicro and Vultr to talk token costs, and our shared vision for an appliance-style AI deployment model. The panel discusses open-source model ecosystems, proof-of-concept offerings, and why this partnership delivers faster time-to-market for enterprise AI.
Watch here on YouTube
Local-first coding assistance
GLM-5.2 and other open coding models running on on-prem AMD Instinctâ„¢ GPUs, served to Claude Code, Cursor, Codex, and Visual Studio Code through standard inference APIs.
Frontier fallback happens under policy
When a request exceeds local model capability or falls inside a sensitivity threshold, the router falls back to Anthropic, OpenAI, Google, or xAI under the rules the platform team defines.
Cost visibility and governance
Token metering, per-team quotas, audit trails, and side-by-side local vs. frontier dashboards for the platform team to see what is being spent, on what, and by whom.
Get started
Ready to retain control over your token spend and maintain data sovereignty without having to endure the GPU waiting period?