LLM Inference Quantization: 4-bit and 8-bit Guide
Understand weight quantization, formats, accuracy tradeoffs, KV cache memory, and how to validate a quantized deployment.
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 1–5 of 5 published articles
Stable Diffusion WebUI: GPU Optimization and Settings
A practical image-generation setup covering VRAM, precision, attention backends, resolution, and safe remote access.
A100 40GB vs A100 80GB: What's the Difference?
Why the memory capacity in an A100 configuration changes model fit, context headroom, and concurrency planning.
How Much GPU VRAM Do You Need for LLM Inference?
A memory-planning checklist for weights, quantization, context windows, KV cache, and concurrency.
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.