Topic
GPU Inference & AI Hardware
5 posts on gpu inference & ai hardware — part of glossary on the n4n AI blog.
What is quantization and how it reduces GPU memory use
Quantization compresses LLM weights from 16-bit to 4-bit or lower, cutting GPU memory use by 2–4× with minimal quality loss — here's how it works and when to use it.
What is GPU inference and how does it work
GPU inference explained for engineers — what it is, how parallel execution works, why it matters for LLM serving, and the misconceptions that trip up teams.
How memory bandwidth affects LLM token generation speed
Understand how memory bandwidth limits LLM token generation speed, with practical techniques to measure, diagnose, and optimize inference throughput on GPU hardware.
H100 vs A100: which GPU is better for LLM inference
A practitioner's head-to-head comparison of H100 vs A100 for LLM inference — specs, throughput, cost models, and which to pick for your workload.
CPU vs GPU inference: why LLMs need parallel compute
A practitioner's comparison of CPU vs GPU inference for LLMs — architecture, latency, cost, quantization, and when to choose each.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88