To recap: On-Premise: your data center, Confidential Computing infrastructure with GPUs required. On-Device: your hardware, fully offline, built for edge. VPC (AWS/GCP): all models and ElevenAgents, your cloud boundary, data stays in your environment. Cloud API: all models
COMPUTING
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On-Premise and On-Device AI Access Launches Mid-2026
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On-Premise and On-Device are in early access, with initial releases expected in the first half of 2026. VPC deployments are available now. Join the waitlist:
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On-Premise GPU Computing for Government and Secure Organizations
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On-Premise runs on your own servers, in your own data center, on Confidential Computing infrastructure with GPUs. This is best suited to government agencies and organizations that cannot procure cloud infrastructure in their required region.
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On-Device AI Inference for Offline Embedded Applications
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On-Device runs directly on the hardware itself and is built for offline inference on constrained compute. This is best suited to use cases that require offline inference, such as automotive manufacturers embedding voice into vehicles or wearables.
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Cloud cost optimization aligning consumption with enterprise value
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Cloud cost optimization isn’t about cutting spend—it’s about aligning consumption with value. https://
tinyurl.com/5y2ct8tk via @LinkedIn #ArtificialIntelligence #MachineLearning #GenerativeAI #EnterpriseAI #CloudComputing #DataPlatforms #CIO #CTO #ChiefDataOfficer #ExecutiveLeadership -
Britain needs cheap power for compute sovereignty and economic competitiveness
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“True sovereignty requires a radical shift to dedicated, low-cost power for compute. Without cheap energy, Britain won’t just lose its factories — it may lose its offices, too.”
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SF and Texas AI Ecosystems: Scale Intelligence and Future Industries
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They are in synchronicity, as the SF ecosystem still powers the AI labs and the venture. ‘American Shenzhen’ IE, Texas, provides the foundation to scale Intelligence, and to build for the Industries of the future (space, fabs etc.) Although, if CA proceeds with wealth tax etc,
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MIT Researchers Cut AI Model Training Costs With CompreSSM
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Training a large AI model is expensive, but MIT CSAIL researchers have helped develop a new approach that cuts compute costs. Using control theory, "CompreSSM" compresses models during training, shedding complexity. It makes models leaner & faster: https://
bit.ly/4sU2Sjc -
Deploy Ultralytics YOLO Models on Axelera Metis AIPUs
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The recording of our live session with @ultralytics and Innowise is now up. See how to deploy Ultralytics YOLO models on Axelera Metis AIPUs in minutes using a single command. Read the technical blog: https://
eu1.hubs.ly/H0tm6sp0
Watch the session: https://
eu1.hubs.ly/H0tm3BT0 #EdgeAI -
AI agent autonomously probes its own multi-GPU setup and stats
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running Qwen3.5 397B MoE (17B active/token)
— Ahmad (@TheAhmadOsman) 9 avril 2026
on 4x DGX Sparks in FP8 (~400GB)
> OpenCode driving
> agent exploring its own config
> probing all 4 Sparks (via ssh) + reporting thermals
> inspecting how vLLM is serving it
> collecting + analyzing its own stats
local AI is awesome https://t.co/KU9u30GgXk pic.twitter.com/yPWSbSKto8running Qwen3.5 397B MoE (17B active/token) on 4x DGX Sparks in FP8 (~400GB) > OpenCode driving
> agent exploring its own config
> probing all 4 Sparks (via ssh) + reporting thermals
> inspecting how vLLM is serving it
> collecting + analyzing its own stats local AI is awesome