Overview
Here's how it works:
Phase 1 - Discovery (1 week): Review your NVIDIA GPU-based ML setup, focusing on algorithms, training loops, and datasets. Establish benchmarks and success metrics compared to your current CUDA models.
Phase 2 - Neuron Porting & Model Deployment (4-8 weeks): Our team ports your ML models to AWS's Neuron and deploys them on Trainium instances, aiming for an isolated performance benchmark against NVIDIA GPUs.
Phase 3 - Benchmarking (1-2 weeks): Conduct a detailed performance and cost comparison between your Neuron models on Trainium and the NVIDIA GPU models. A conclusive report outlines benchmark reporting, comparisons and migration strategies to AWS silicon.
Highlights
- Deliverables to simplify your decision-making: - Direct comparison of AWS Silicon vs. NVIDIA GPUs: Focused on cost, performance, and efficiency. - Neuron-based model architecture: Tailored to optimize your ML workflows on AWS. - Comprehensive benchmarking report: Detailed insights and a roadmap for AWS Silicon adoption. - Strategic migration guidance: From pilot setup to full-scale deployment on AWS.
Details
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To schedule your free Consultation contact Loka at AWS-team@loka.com , or your AWS representative.