Llama-3.1-Nemotron-Ultra-253B

Name: Llama-3.1-Nemotron-Ultra-253B
Author: Meta AI

by Meta AILlama 3.1

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

Released May 2025context N/A253B params

Nemotron-Ultra-253B is a heavyweight thinker that trades raw speed for depth, excelling at complex reasoning, coding, and instruction-following tasks. Built on Meta's Llama 3.1 architecture but heavily fine-tuned by NVIDIA, it behaves like a careful analyst — methodical and thorough rather than quick and breezy. Its massive parameter count means it demands serious compute, but rewards that investment with notably strong multi-step reasoning.

Capabilities

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

Reasoning & Logic

Moderate

Coding

Llama-3.1-Nemotron-Ultra-253B

by Meta AILlama 3.1

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

Released May 2025context N/A253B params

Nemotron-Ultra-253B is a heavyweight thinker that trades raw speed for depth, excelling at complex reasoning, coding, and instruction-following tasks. Built on Meta's Llama 3.1 architecture but heavily fine-tuned by NVIDIA, it behaves like a careful analyst — methodical and thorough rather than quick and breezy. Its massive parameter count means it demands serious compute, but rewards that investment with notably strong multi-step reasoning.

Capabilities

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

Reasoning & Logic

Moderate

Coding

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Complex ReasoningThe ability to work through multi-step problems, analyze nuanced information, and draw logical conclusions.Fine-TunedA pre-trained model further trained on a smaller, task-specific dataset to improve performance on that task.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Multi-Step ReasoningThe ability to break down complex problems into smaller steps and solve them sequentially, rather than jumping directly to an answer.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.

Capabilities

Capabilities

Use Case Fit

Glossary