NVIDIA Nemotron 3 Nano 4B FP8

Name: NVIDIA Nemotron 3 Nano 4B FP8
Author: NVIDIA

by NVIDIANVIDIA

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

Released March 2026262K context≈ 196,608 words

A compact, efficient model from NVIDIA's Nemotron family, running in FP8 precision to keep memory footprint small without sacrificing too much capability. Its 262K token context window is notably generous for a 4B parameter model, making it capable of processing long documents despite its small size. The FP8 quantization means it trades some numerical precision for speed and resource efficiency.

Capabilities

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

Long Context

Strong

Reasoning & Logic

NVIDIA Nemotron 3 Nano 4B FP8

by NVIDIANVIDIA

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

Released March 2026262K context≈ 196,608 words

A compact, efficient model from NVIDIA's Nemotron family, running in FP8 precision to keep memory footprint small without sacrificing too much capability. Its 262K token context window is notably generous for a 4B parameter model, making it capable of processing long documents despite its small size. The FP8 quantization means it trades some numerical precision for speed and resource efficiency.

Capabilities

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

Long Context

Strong

Reasoning & Logic

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.FP8 PrecisionA data format that stores numbers using 8 bits instead of the standard 32 bits, significantly reducing memory requirements with minimal quality loss.FP8 QuantizationA compression technique that reduces model size by representing weights using 8-bit floating-point numbers instead of higher precision, making it faster and more memory-efficient.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Parameter ModelA neural network described by the number of learnable weights it contains; more parameters generally mean greater capacity to learn complex patterns, but also require more computational resources.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.

Capabilities

Capabilities

Use Case Fit

Glossary