cluster
GPU Selection
Choosing GPUs for a workload: VRAM sizing, A100 vs H100 vs B200 comparisons, node configurations and available GPU types.
23 articles
Subclusters
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 21, 2026
Mixture of Experts VRAM Requirements: A Practical Guide for ML Teams
Mixture of Experts (MoE) architectures promise massive intelligence at a fraction of the compute cost. But when moving from research to production, ML teams quickly discover the hidden bottleneck: MoE models are ruthlessly memory-bound.
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 14, 2026
GPU Selection Guide: Inference vs. Training Workloads in 2026
Selecting the wrong GPU architecture inflates your cost per token or bottlenecks your training runs. Understanding the structural differences between inference and training workloads is the only way to right-size your 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.
May 10, 2026
Best GPU for LLM Fine-Tuning in 2026: Benchmarks & VRAM Math
Stop guessing your VRAM requirements. We break down the exact math, real-world benchmarks, and infrastructure economics for fine-tuning LLMs on NVIDIA B200, H100, A100, and L40S GPUs.
March 11, 2026
NVIDIA B200 180GB VRAM Model Requirements: A Technical Guide
The NVIDIA B200 introduces 180GB of HBM3e memory and native FP4 precision, fundamentally changing how AI teams provision infrastructure. Understanding its exact memory requirements is critical to preventing out-of-memory errors and maximizing cluster utilization.
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
Best GPU for Llama 3 Fine-Tuning: A Technical Engineering Guide
Fine-tuning Llama 3 requires a precise balance of VRAM capacity and memory bandwidth to avoid the dreaded Out-of-Memory errors. This guide breaks down the hardware requirements for 8B and 70B models, focusing on cost-efficient scaling and sovereign infrastructure.
February 23, 2026
GPU for 7B vs 70B Model: A Technical Infrastructure Guide
Choosing between 7B and 70B models is not just a performance decision, it is a fundamental shift in infrastructure requirements. This guide breaks down the hardware specifications, memory constraints, and orchestration strategies needed to deploy these models efficiently.
February 23, 2026
GPU Memory Requirements for Transformer Models: A Technical Guide
Understanding the exact memory footprint of Transformer architectures is the difference between a successful deployment and a frustrating Out-of-Memory (OOM) error. We break down the math behind weights, activations, and optimizer states to help you size your GPU clusters accurately.
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.
February 23, 2026
How Much VRAM for a 70B Model? A Technical Engineering Guide
Deploying 70B parameter models like Llama 3 requires a precise understanding of VRAM allocation beyond simple weight storage. This guide breaks down the memory overhead for different precision levels and training configurations to help you optimize your GPU infrastructure.
February 23, 2026
Which GPU for Fine-Tuning 70B Models? A Technical Guide
Fine-tuning a 70B parameter model is the ultimate test for AI infrastructure. This guide breaks down the hardware requirements, from VRAM math to multi-GPU orchestration, ensuring you don't waste budget on underpowered or overprovisioned clusters.
January 28, 2026
Hardware Recommendations for LLM Fine-Tuning: The 2026 Guide
Selecting the wrong hardware for LLM fine-tuning leads to Out-of-Memory errors and wasted compute cycles. This guide breaks down the technical requirements for modern architectures like Llama 4 and Mistral to ensure your infrastructure matches your model's scale.
January 26, 2026
How Many GPUs for Model Training? A Practical Scaling Guide
Throwing more hardware at a model does not always lead to faster convergence. We break down the math behind GPU scaling to help you avoid over-provisioning and maximize training efficiency while maintaining data sovereignty.
January 23, 2026
GPU Selection Guide for ML Training: 2026 Performance Benchmarks
Choosing the wrong GPU cluster doesn't just waste budget, it kills momentum through Out-of-Memory errors and scaling bottlenecks. This guide breaks down the 2026 hardware landscape to help you architect for efficiency and data sovereignty.
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.
January 14, 2026
How to Right Size GPU Instances for ML Workloads
Most engineering teams waste a significant share of their compute budget on over-provisioned GPUs or lose days of productivity to Out-of-Memory errors. Finding the balance between VRAM capacity and compute throughput is the difference between a successful deployment and a drained runway.
December 26, 2025
How to Predict VRAM Usage for PyTorch Models
The dreaded CUDA Out of Memory error is not a random occurrence but a predictable failure in resource planning. Understanding the exact byte-level requirements of your model allows you to optimize performance and maintain infrastructure independence.
December 24, 2025
GPU Memory Calculator for Deep Learning: A Technical Guide
Running out of memory mid-training is a costly engineering failure that stalls innovation. Understanding the precise breakdown of weights, gradients, and optimizer states is the only way to optimize your compute budget and avoid the dreaded CUDA Out of Memory error.
December 15, 2025
GPU Memory Estimation: A Guide to VRAM Requirements
Out-of-memory (OOM) errors are the silent killers of training productivity and budget. Learn how to mathematically predict your GPU memory footprint before you provision a single node on your cluster.