LOCI Optimization Agentic AI is a goal-oriented, autonomous-ready agent for AI infrastructure lifecycle management, that optimizes proprietary GPUs, CPUs, and accelerators. LOCI continuously analyzes GPUs and CPUs, predicts performance and stability risks, and applies optimizations before they hit digital twin or production.
Improves GPU/CPU utilization and reduces power costs and identifies high-cost basic blocks in code.
Define the goal LOCI applies batch tuning, quantization (Q1’26), and code rewrites automatically
from large-scale model serving to resource-constrained edge device.
Native CPU & GPU profiling, kernel-level insights, and serving-layer awareness.
Identifies latency drifts, power spikes, and bottlenecks before they affect testing and deployment
Integrates into pipelines, MLOps, and serving stacks without disrupting production.
Scans compiled Binary files & configs for potential bottlenecks.
Hardware-native reasoning and optimization decisions.
Deploys enhanced version with predictive fixes.
Continuous learning from production data.
Autonomous system tuning across performance, power, and cost goals.
Examples:
Reduce runtime costs by 20%+
Cut latency by 27% in production AI pipelines
Optimize application layer, cost reduction and Revenues generation - Increases serving per HW
GitHub Copilot-like services achieve faster response times and lower operational costs
IoT and autonomous devices gain power efficiency improvements
Scientific computing achieves maximum utilization with shorter runtimes
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