Running NVIDIA Nemotron 3.5 Lightning on an RTX 5090: Practical Considerations
A deployment checklist for a named model family: verify its exact publisher card, representation, memory plan, and serving stack.
Hostnot GPU engineering
GPU, AI, and infrastructure guides
Practical notes for choosing hardware, connecting safely, and building dependable AI workloads. Every guide reflects what the platform actually does today.
Showing 37–45 of 45 published articles
Best GPU Types for AI Inference: A Practical Guide
How to evaluate GPU classes for inference without treating a hardware name as a universal recommendation.
How to Choose a GPU for LLM Inference
A repeatable selection process for model fit, context policy, concurrency, framework support, and cost control.
How to Connect to a Cloud GPU with SSH
A security-first connection workflow using an SSH key, allocated port, and host fingerprint verification.
Serverless GPU vs Dedicated GPU: Which Should You Use?
Choose the operating model that matches workload persistence, customization, access needs, and metering.
How Much GPU VRAM Do You Need for LLM Inference?
A memory-planning checklist for weights, quantization, context windows, KV cache, and concurrency.
NVIDIA H100 vs H200: Which GPU Should You Choose?
A workload-first framework for choosing between accelerator classes with different memory and platform characteristics.
RTX 5090 vs NVIDIA A100 for AI Workloads
Compare workload fit, memory planning, software validation, and operational tradeoffs without relying on synthetic benchmark claims.
RTX 5090 for AI: VRAM, Inference and Workload Guide
A practical way to evaluate a high-memory consumer GPU for local development, inference, and batch work.