Eduardo Alvarez

Eduardo Alvarez is a senior technical lead at NVIDIA, where he focuses on AI inference at scale, performance optimization, workload economic analysis, and application enablement. He has a deep background in AI systems engineering, workload optimization, and accelerated computing—focused on translating innovations into real-world applications. Before NVIDIA, Eduardo held engineering roles at various semiconductor and energy tech companies.
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Posts by Eduardo Alvarez

Data Center / Cloud

Top 5 AI Model Optimization Techniques for Faster, Smarter Inference

As AI models get larger and architectures more complex, researchers and engineers are continuously finding new techniques to optimize the performance and... 6 MIN READ
Data Center / Cloud

Optimizing Inference for Long Context and Large Batch Sizes with NVFP4 KV Cache

Quantization is one of the strongest levers for large-scale inference. By reducing the precision of weights, activations, and KV cache, we can reduce the memory... 10 MIN READ
Agentic AI / Generative AI

NVIDIA-Accelerated Mistral 3 Open Models Deliver Efficiency, Accuracy at Any Scale 

The new Mistral 3 open model family delivers industry-leading accuracy, efficiency, and customization capabilities for developers and enterprises. Optimized... 6 MIN READ
Data Center / Cloud

Making GPU Clusters More Efficient with NVIDIA Data Center Monitoring Tools

High-performance computing (HPC) customers continue to scale rapidly, with generative AI, large language models (LLMs), computer vision, and other uses leading... 9 MIN READ
Data Center / Cloud

Scaling Large MoE Models with Wide Expert Parallelism on NVL72 Rack Scale Systems

Modern AI workloads have moved well beyond single-GPU inference serving. Model parallelism, which efficiently splits computation across many GPUs, is now the... 10 MIN READ
Agentic AI / Generative AI

How Quantization Aware Training Enables Low-Precision Accuracy Recovery

After training AI models, a variety of compression techniques can be used to optimize them for deployment. The most common is post-training quantization (PTQ),... 10 MIN READ