Tendance Aktualisiert 2026-04

Neural Scaling Laws

Definition

Neural scaling laws are predictable mathematical relationships linking an AI model's performance to its size, training data volume and compute budget.

Siehe auch im Glossar

L
LLM (Large Language Model)
An LLM is an AI model trained on billions of texts, capable of understanding and generating human language.
D
Deep Learning
Deep Learning is a subset of Machine Learning using multi-layered neural networks to learn complex representations from raw data.
A
AI Benchmark
An AI benchmark is a standardized test that measures and compares AI model performance on specific tasks.
G
GPU Cloud
GPU Cloud provides on-demand graphics processors for training and running AI models without hardware investment.
F
Foundation Model
A foundation model is a large AI model pre-trained on massive data, adaptable to multiple tasks.
T
Test-time Compute
Test-time compute allocates more computation at inference time to improve response quality, instead of just scaling up the model.

Tools, die neural scaling laws verwenden

C
Claude
Die KI, die Nuancen versteht – von Anthropic
4.7/5
G
Gemini
Googles KI-Assistent mit 1-Million-Token-Kontext
4.5/5
M
Meta AI (Llama)
Metas KI-Assistent, betrieben von Llama – dem führenden Open-Source-LLM
4.3/5
Q
Qwen
Alibabas LLM mit Stärken in Code und Mehrsprachigkeit
4.4/5

Häufig gestellte Fragen

Do scaling laws still hold in 2026?
Generally yes, but with nuances. Classic scaling laws (more compute = better model) still hold, but marginal returns are diminishing. Inference-time scaling laws (test-time compute) represent a new optimization frontier.
What does the Chinchilla law predict?
The Chinchilla law (DeepMind, 2022) predicts that an optimally trained model should have roughly 20 tokens of data per parameter. This means a 70 billion parameter model should be trained on about 1.4 trillion tokens to be compute-optimal.