Llama 3.2 1B Instruct quantized.w8a8

Llama

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

Released September 2024131K context≈ 98,304 words1B params

A compact, efficient model that trades raw capability for speed and reduced memory footprint. The w8a8 quantization means both weights and activations are compressed to 8-bit, making it noticeably faster and lighter than its full-precision counterpart, with some accuracy trade-off. It handles instruction-following tasks within a generous 128k context window, suited for environments where compute resources are constrained.

Capabilities

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

Creative Writing

Moderate

Coding

Llama 3.2 1B Instruct quantized.w8a8

Llama

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

Released September 2024131K context≈ 98,304 words1B params

A compact, efficient model that trades raw capability for speed and reduced memory footprint. The w8a8 quantization means both weights and activations are compressed to 8-bit, making it noticeably faster and lighter than its full-precision counterpart, with some accuracy trade-off. It handles instruction-following tasks within a generous 128k context window, suited for environments where compute resources are constrained.

Capabilities

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

Creative Writing

Moderate

Coding

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.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.W8A8 QuantizationA specific quantization method where both weights (w) and activations (a) are stored as 8-bit integers, providing a good balance between memory savings and model quality.W8A8 QuantizationA specific quantization method that reduces both weights and activations to 8-bit integers, enabling faster computation on specialized hardware while maintaining reasonable accuracy.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary