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Articles
January 19, 2026
A100 vs H100 for LLM Inference: The Engineer’s Guide to Efficiency
Choosing between the NVIDIA A100 and H100 is no longer just a question of budget. For engineers building the next generation of AI applications, it is a choice between two fundamentally different architectural approaches to the transformer block. The A100 was the workhorse of the first LLM wave, but the H100 was built specifically to solve the bottlenecks that emerged during that era. At Lyceum, we see teams struggling with OOM errors and high latency because they are trying to force modern, high-parameter models onto older hardware without considering the total cost of inference. This guide breaks down the technical reality of these GPUs to help you optimize your deployment.
May 15, 2026
NVIDIA H200 vs H100 Cost Performance Comparison
The NVIDIA H200 offers 76% more memory than the H100, but identical compute power. Discover exactly when the H200's higher hourly rate is justified for your AI infrastructure.
May 10, 2026
NVIDIA B200 vs H100 Inference Performance Benchmarks
Inference now dominates AI compute spend. If you are serving 70B+ parameter models, the architectural leap from Hopper to Blackwell fundamentally changes your unit economics.
March 11, 2026
NVIDIA B200 vs H200 GPU for Inference: Architecture & Benchmarks
Choosing between the NVIDIA B200 and H200 dictates your inference latency and Total Cost of Compute. Discover how Blackwell's dual-die architecture and native FP4 support compare to Hopper's refined HBM3e memory.
March 11, 2026
H100 vs B200 GPU Cost Efficiency Comparison for AI Workloads
Choosing the right GPU architecture dictates both the speed of your AI development and the sustainability of your infrastructure budget. Understanding the exact cost efficiency differences between the H100 and B200 is critical for optimizing large-scale machine learning workloads.
February 23, 2026
H100 80GB vs A100 80GB: Fine-Tuning Performance and TCC Analysis
Choosing between the NVIDIA H100 and A100 for fine-tuning involves more than comparing VRAM capacity. While both offer 80GB, the architectural shift to Hopper introduces the Transformer Engine and FP8 support, fundamentally altering the throughput and cost-efficiency of modern AI workloads.
January 21, 2026
H100 vs A100 Cost Efficiency: A Technical Deep Dive
Stop looking at hourly rates and start measuring cost-per-checkpoint. We break down why the H100's architectural leaps make it the superior choice for modern AI workloads despite the higher price tag.