NVIDIA Nemotron Labs 3 Puzzle 75B A9B NVFP4

Name: NVIDIA Nemotron Labs 3 Puzzle 75B A9B NVFP4
Author: NVIDIA

by NVIDIA

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

Released July 2026262K context≈ 196,608 words75B params

A large sparse model with a 9B active parameter footprint despite its 75B total size, meaning it punches with efficiency on each token while carrying broad knowledge in its inactive weights. It operates on NVFP4 precision, NVIDIA's own numeric format, which ties it closely to NVIDIA hardware for optimal performance. The 'Puzzle' naming suggests a specialized focus, but concrete details about its task orientation are limited beyond its text-in, text-out design.

Capabilities

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

Long Context

Exceptional

Reasoning & Logic

NVIDIA Nemotron Labs 3 Puzzle 75B A9B NVFP4

by NVIDIA

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

Released July 2026262K context≈ 196,608 words75B params

A large sparse model with a 9B active parameter footprint despite its 75B total size, meaning it punches with efficiency on each token while carrying broad knowledge in its inactive weights. It operates on NVFP4 precision, NVIDIA's own numeric format, which ties it closely to NVIDIA hardware for optimal performance. The 'Puzzle' naming suggests a specialized focus, but concrete details about its task orientation are limited beyond its text-in, text-out design.

Capabilities

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

Long Context

Exceptional

Reasoning & Logic

Glossary

NVFP4 PrecisionA low-precision numerical format optimized by NVIDIA that uses fewer bits per number than standard formats, enabling efficient inference on NVIDIA GPUs while maintaining reasonable accuracy.NVFP4 PrecisionA low-precision numerical format that uses 4 bits per weight, developed by NVIDIA to compress models for efficient inference on consumer hardware.Parameter FootprintThe total number of learnable weights in a model, which directly affects its memory requirements and computational cost — smaller footprints run faster on consumer devices.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.Sparse ModelA model that activates only a subset of its parameters for each input, rather than using all parameters every time, which reduces computational cost.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary