llama 3.3 70b instruct fp16

Name: llama 3.3 70b instruct fp16
Author: Meta AI

by Meta AILlama 3.3

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

Released March 2025131K context≈ 98,304 words70b params

A capable and articulate text model that punches at a high level for its size, handling complex reasoning, coding, and instruction-following with consistency. The fp16 precision preserves the full numerical fidelity of the original weights, meaning responses tend to be more reliable than quantized variants at the cost of higher memory demands — expect to need substantial VRAM. It converses naturally and handles multi-step tasks without losing the thread.

Capabilities

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

Reasoning & Logic

Moderate

Multimodal

llama 3.3 70b instruct fp16

by Meta AILlama 3.3

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

Released March 2025131K context≈ 98,304 words70b params

A capable and articulate text model that punches at a high level for its size, handling complex reasoning, coding, and instruction-following with consistency. The fp16 precision preserves the full numerical fidelity of the original weights, meaning responses tend to be more reliable than quantized variants at the cost of higher memory demands — expect to need substantial VRAM. It converses naturally and handles multi-step tasks without losing the thread.

Capabilities

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

Reasoning & Logic

Moderate

Multimodal

Glossary

Complex ReasoningThe ability to work through multi-step problems, analyze nuanced information, and draw logical conclusions.FP16 PrecisionA data format that stores model weights using 16-bit floating-point numbers, preserving full model accuracy while using less memory than 32-bit formats.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Multi-Step TasksProblems or workflows that require a model to perform multiple sequential operations or reasoning steps to reach a final answer.Numerical FidelityThe accuracy and precision with which a model preserves mathematical calculations; lower fidelity means some precision is lost, often as a trade-off for smaller model size.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.VRAMVideo RAM — the memory on a GPU that stores model weights and intermediate computations during inference.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary