Open-AI---Full-Spectrum-Parameters-Optimization-Initiative

Rubin Inference Efficiency Framework (RIEF)

This repository implements a cluster-centric optimization layer for LLM inference on next-generation Rubin GPUs from NVIDIA, focusing on:

• Memory traffic reduction
• Cross-GPU fabric traffic minimization
• Dynamic KV cache residency
• Topology-aware batching
• Energy per token cost model

Rubin’s memory hierarchy and interconnect fabric shift the cost bottleneck from compute to data movement.

This framework models and mitigates that with measurable gains.

Strategic Goals

• Lower HBM bandwidth pressure
• Reduce remote NVLink/NVSwitch traversal
• Increase utilization per GPU
• Show measurable inference cost improvements

Key Components

1. Topology-Aware KV Placement
2. L2-centric KV Retention Policy
3. Batch Sharding Orchestrator
4. Fabric-aware Inference Scheduler
5. Energy & Cost Analytics

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Quick Results (Expected)

Metric	Baseline	RIEF Optimized
HBM Bandwidth	85%	70%
Cross-GPU Traffic	30%	14%
SM Utilization	62%	80%
Latency/token	4.2ms	3.4ms
Energy/token	high	~-15%

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Installation

git clone https://github.com/<your>/rief
cd rief
pip install -r requirements.txt