Tag: LLM inference

Confidential Computing for LLM Inference: Securing Data and Models with TEEs

by Phillip Ramos

Discover how confidential computing and TEEs protect LLM data in use. Learn about NVIDIA GPU encryption, performance trade-offs, and regulatory benefits for secure AI.

vLLM vs TGI: Which LLM Serving Framework Should You Use in 2026?

by Phillip Ramos

Compare vLLM and TGI for LLM serving. Learn about PagedAttention, throughput benchmarks, and which framework fits your API's latency and scale needs.

GPU Selection for LLM Inference: A100 vs H100 vs CPU Offloading

by Phillip Ramos

Learn how to choose between NVIDIA A100, H100, and CPU offloading for LLM inference in 2025. See real performance numbers, cost trade-offs, and which option actually works for production.

Transformer Efficiency Tricks: KV Caching and Continuous Batching in LLM Serving

by Phillip Ramos

KV caching and continuous batching are essential for fast, affordable LLM serving. Learn how they reduce memory use, boost throughput, and enable real-world deployment on consumer hardware.