Llama 4 Scout 17B 16E Instruct quantized.w4a16

Name: Llama 4 Scout 17B 16E Instruct quantized.w4a16
Author: Meta AI

by Meta AILlama

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

Released April 2025context N/A17B params

Scout is a multimodal open-weight model that handles both text and image inputs, built on a mixture-of-experts architecture with 16 experts and 17B parameters. It runs in a quantized W4A16 format, meaning weights are compressed to 4-bit precision while activations stay at 16-bit — a trade-off that reduces memory footprint at some potential cost to output fidelity. It behaves like a capable generalist that's been made more portable for deployment on constrained hardware.

Capabilities

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

Coding

Moderate

Tool Use

Llama 4 Scout 17B 16E Instruct quantized.w4a16

by Meta AILlama

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

Released April 2025context N/A17B params

Scout is a multimodal open-weight model that handles both text and image inputs, built on a mixture-of-experts architecture with 16 experts and 17B parameters. It runs in a quantized W4A16 format, meaning weights are compressed to 4-bit precision while activations stay at 16-bit — a trade-off that reduces memory footprint at some potential cost to output fidelity. It behaves like a capable generalist that's been made more portable for deployment on constrained hardware.

Capabilities

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

Coding

Moderate

Tool Use

Glossary

4-bit PrecisionA quantization level where model weights are stored using only 4 bits per value, significantly reducing model size at the cost of some accuracy.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Bit PrecisionThe number of bits used to represent each number in a model; lower bit precision (like 3-bit) means smaller file size but potentially less accurate calculations.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.MultimodalA model that can process and understand multiple types of input, such as both text and images.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.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.W4A16A quantization format where model weights are stored in 4-bit precision while calculations use 16-bit precision, balancing efficiency with accuracy.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary