ScitiX Introduces Enterprise AI Inference Platform
ScitiX announced in a press release the full scope of its production inference platform for enterprises running AI workloads at scale. The platform runs on ScitiX owned and operated NVIDIA B200, H200, and H100 infrastructure.
ScitiX said the service supports open source, tuned, and third party models through familiar APIs. It includes model routing and fallback, session aware context reuse, fault tolerant execution, private deployment environments, zero retention policies, and observability with telemetry, audit logs, and performance dashboards.
The company reported current production metrics of more than 1 trillion tokens processed daily, average time to first token of about one second, cache hit rates above 90%, and 99.9% uptime.
ScitiX Model Inference is available now to enterprise customers. The company said deployment options, pricing, and supported model information are available through its inference page.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Enterprise AI Brief or Daily AI Brief.
Also, consider following us on social media:
More from Enterprise
Oct 16 BOC Group Adds AI Assistant to ADOIT 21 Oct 16 Huawei Brings Upgraded Stellar Network Solution to Europe Oct 16 Element to Manage Waymo's Toronto Fleet for 2028 Launch Oct 16 CTM and Huawei Plan AI Network Security Upgrades in Macao Oct 16 Huawei and 14 partners form Global AI CITY allianceEnterprise AI Brief
Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Saturn Cloud Integrates NVIDIA Run:ai for GPU Inference Services
Corvex Launches Token Factory for Open Weight AI Inference
Architect Launches Liquid Inference Auction Platform
Senao Networks Launches x86 Servers for Enterprise AI
SuperX Adds Model as a Service to Singapore AI Centre
Daily AI Brief: the AI news that matters, in your inbox.