One-Liner
An AI workload scheduler that optimizes for HBM memory constraints rather than GPU compute, increasing inference throughput without additional hardware.
AI Thinking Process
Memory-First AI Workload Scheduling. HBM now the binding constraint, not GPU compute. Most LLM inference is memory bandwidth-bound. Existing schedulers (Kubernetes, SLURM, Ray) optimize for GPU availability, not memory bandwidth.
NVIDIA's Triton Inference Server is the dominant platform. Adding memory-first scheduling is a natural feature. NVIDIA has the most visibility into HBM usage patterns across their own hardware. Feature gravity confirmed. Structural independence test fails: hardware vendor benefits from adding this and can technically add it.
Feature gravity toward NVIDIA/Google/AMD. Memory scheduling layer sits inside inference runtime owned by hardware vendors. No structural independence possible.
Kill Reason
Feature gravity toward NVIDIA, Google, and AMD. The memory scheduling layer sits inside the inference runtime which is owned by hardware vendors. NVIDIA has the most visibility into HBM usage across their hardware and every incentive to add memory-first scheduling because it directly increases GPU sales.
Risk Analysis
Risk analysis available for latest engine ideas.
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