Nemotron H 4B Instruct 128K

Name: Nemotron H 4B Instruct 128K
Author: NVIDIA

by NVIDIANemotron

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2025131K context≈ 98,304 words4B params

A compact but surprisingly capable model that punches above its weight class for its size. Built on NVIDIA's hybrid Mamba-Transformer architecture, it handles long contexts up to 128K tokens efficiently — a rare feat at 4B parameters. It trades raw reasoning depth for speed and memory efficiency, making it practical for resource-constrained deployments.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Factual Knowledge

Moderate

Reasoning & Logic

Nemotron H 4B Instruct 128K

by NVIDIANemotron

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2025131K context≈ 98,304 words4B params

A compact but surprisingly capable model that punches above its weight class for its size. Built on NVIDIA's hybrid Mamba-Transformer architecture, it handles long contexts up to 128K tokens efficiently — a rare feat at 4B parameters. It trades raw reasoning depth for speed and memory efficiency, making it practical for resource-constrained deployments.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Factual Knowledge

Moderate

Reasoning & Logic

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Hybrid Mamba-Transformer ArchitectureA neural network design that combines Mamba (a fast, efficient sequence model) with Transformer components to balance speed and capability.MambaA state-space model architecture designed to process long sequences faster and with less memory than traditional transformer models.Mamba-Transformer ArchitectureA hybrid model design that combines Mamba (a state-space model) with Transformer components to process long sequences more efficiently than pure Transformers while maintaining strong performance.Memory EfficiencyHow well a model uses available RAM or GPU memory, allowing it to run on smaller or less expensive hardware.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning DepthA model's ability to perform complex multi-step logical thinking and problem-solving; typically increases with model size.Resource-ConstrainedHardware with limited memory, processing power, or battery life, requiring models to be optimized for efficiency.TokensThe basic units of text that a language model processes, typically representing words or word fragments.TransformerThe dominant neural network architecture for language models, using self-attention to process sequences.Transformer ArchitectureA neural network design that processes text by analyzing relationships between all words simultaneously, forming the foundation of modern large language models.

Capabilities

Capabilities

Use Case Fit

Glossary