UNIVERSITAS AI
UNIVERSITAS AI
Advancing the science of reliable intelligence.
From memory-first inference to autonomous agent coordination.
AI research has undergone three architectural inversions between 2023-2025. We're building the infrastructure for this new reality.
Long-context inference (≥16K tokens) exhibits arithmetic intensity collapse, making memory bandwidth—not compute throughput—the performance ceiling.
AMD MI300X (192GB, 5.3 TB/s) achieves 112% of NVIDIA H100 performance on 32K-context workloads despite 34% lower FLOPs.
Reinforcement learning in verifiable domains now delivers 37-40× higher capability returns per dollar than pre-training scaling.
DeepSeek R1 achieves 79.8% on AIME mathematics (vs GPT-4's 13.4%) through $8-15M RL on a $5.6M pre-trained base.
For production models, 90%+ of total cost is serving, not training. Reasoning models generating 10K+ token outputs cost 185× more per query.
Meta allocates 97.3% of 600K GPUs to inference rather than training, fundamentally reshaping infrastructure requirements.
From Compression to Abundance
Memory-first paradigm, post-training scaling, and reasoning emergence. Hardware inversions that change optimal AI infrastructure.
Measured Impact:
AMD MI300X: 12% faster at 32K context • 108-112% H100 performance • 34% TCO reduction
Distributed Supercomputing
Multi-cloud orchestration, hardware-aware deployment, and cross-cloud state coordination protocols.
Measured Impact:
80% faster cold starts • 40-60% cost savings • 88% faster scaling response
Standards for Autonomous AI
Metadata standards enabling trust, discovery, and autonomous operation across organizational boundaries.
Open Standard:
Apache 2.0 licensed • 10-category metadata schema • Multi-authority verification
TTFT Reduction
Through KV cache coordination
Cost Savings
Via hardware-aware deployment
Faster Scaling
8min → 60s automated response
AIME Score
Via pure RL post-training
Faster Cold Starts
180s → 12s regional caching
Routing Accuracy
Cross-cloud coordination
Constellation, DEO, KVBridge, and ROCmOpt protocols enabling distributed supercomputing.
Hardware inversions and post-training supremacy in frontier AI research.
From single models to ecosystem scale: agent coordination and multi-cloud execution.
Verified AI agent metadata and deployment standards for enterprise coordination.
Hardware-aware model deployment protocol
Universal agent metadata standard
Inference-aware autoscaling for multi-cloud GPU clusters
Cross-cloud KV cache coordination
Affiliate, MBZUAI Institute of Foundation Models
"We're solving the coordination problems that frontier research actually faces today. Not theoretical futures—the fragmented multi-cloud reality researchers navigate right now."
Deploy our protocols in your infrastructure. Open source, Apache 2.0 licensed.
Get StartedSubmit PRs, add papers, join working groups. Build the future of AI infrastructure.
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