meta llama Llama 4 Scout 17B 16E 8bit

Llama

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

Released April 2025context N/A17B params

A mid-sized mixture-of-experts model from Meta's Llama 4 generation, quantized to 8-bit for more accessible deployment. With 17 billion active parameters across 16 experts, it balances capability with memory efficiency. The 8-bit quantization means slightly reduced precision compared to full-weight versions, but enables running on hardware that would otherwise struggle with the full model.

Capabilities

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

Reasoning & Logic

Moderate

Multimodal

Moderate

Multilingual

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

meta llama Llama 4 Scout 17B 16E 8bit

Llama

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

Released April 2025context N/A17B params

A mid-sized mixture-of-experts model from Meta's Llama 4 generation, quantized to 8-bit for more accessible deployment. With 17 billion active parameters across 16 experts, it balances capability with memory efficiency. The 8-bit quantization means slightly reduced precision compared to full-weight versions, but enables running on hardware that would otherwise struggle with the full model.

Capabilities

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

Reasoning & Logic

Moderate

Multimodal

Moderate

Multilingual

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

8-bit QuantizationA specific quantization method that represents model weights using 8 bits instead of the standard 32 bits, significantly reducing memory requirements.Active ParametersThe subset of a model's total parameters that are actually used during inference for each input, as opposed to all parameters being used every time.Memory EfficiencyHow well a model uses available RAM or GPU memory, allowing it to run on smaller or less expensive hardware.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.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.

Capabilities

Use Case Fit

Capabilities

Use Case Fit

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Glossary