Llama 3.1 70B Instruct FP8

Name: Llama 3.1 70B Instruct FP8
Author: NVIDIA

by NVIDIALlama 3.1

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

Released August 2024131K context≈ 98,304 words70B params

A large-scale open-weight model running in FP8 precision, which reduces memory footprint and speeds up inference compared to full-precision variants while preserving most of the original model's capability. It handles long documents comfortably with its 128k context window and follows instructions reliably across a wide range of text tasks. The FP8 quantization means some subtle accuracy trade-offs are possible, but in practice the difference is minimal for most use cases.

Capabilities

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

Reasoning & Logic

Moderate

Instruction Following

Llama 3.1 70B Instruct FP8

by NVIDIALlama 3.1

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

Released August 2024131K context≈ 98,304 words70B params

A large-scale open-weight model running in FP8 precision, which reduces memory footprint and speeds up inference compared to full-precision variants while preserving most of the original model's capability. It handles long documents comfortably with its 128k context window and follows instructions reliably across a wide range of text tasks. The FP8 quantization means some subtle accuracy trade-offs are possible, but in practice the difference is minimal for most use cases.

Capabilities

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

Reasoning & Logic

Moderate

Instruction Following

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.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.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.

Llama 3.1 70B Instruct FP8

Capabilities

Llama 3.1 70B Instruct FP8

Capabilities

Use Case Fit

Key Papers

Glossary