Topic
Model Parameters & Model Size
7 posts on model parameters & model size — part of glossary on the n4n AI blog.
What are LLM parameters, exactly?
A precise technical explanation of LLM parameters — what they are, how they function in transformer architectures, why parameter count correlates with capability, and the misconceptions that trip up engineers.
LLM parameters explained: what a "70B" model means
What "70B" means in LLM model names, how parameter counts map to memory and compute, and what engineers actually need to know when choosing model sizes.
How VRAM requirements scale with parameter count
A practical breakdown of how model parameters translate to GPU memory, covering quantization, KV cache, and real-world GPU fit.
How model size affects inference cost and speed
Understand how model size drives inference latency, memory pressure, and per-token cost — with concrete math and routing strategies for production workloads.
How many parameters does GPT-4 have? What's known
GPT-4's parameter count remains undisclosed by OpenAI. We examine the evidence, architectural clues, and why the number matters less than you think.
Does a bigger model always mean a better model?
Analysis of when larger LLMs outperform smaller ones, covering scaling laws, diminishing returns, inference costs, and practical routing strategies for engineers.
7B vs 70B vs 405B: what model size actually changes
A practitioner's comparison of 7B, 70B, and 405B LLMs across capabilities, cost, latency, deployment, and ecosystem — with a clear verdict for each use case.
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