Meta Llama 3.1 8B Instruct quantized.w8a8

Llama 3.1

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

Released October 2022131K context≈ 98,304 words8B params

A compact, efficient text model that punches above its weight class for an 8B parameter system. The w8a8 quantization (8-bit weights and activations) keeps memory footprint small while preserving much of the original model's capability. Expect slightly reduced precision compared to the full-precision version, but solid general instruction-following across a wide context window.

Capabilities

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

Reasoning & Logic

Moderate

Multimodal

Meta Llama 3.1 8B Instruct quantized.w8a8

Llama 3.1

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

Released October 2022131K context≈ 98,304 words8B params

A compact, efficient text model that punches above its weight class for an 8B parameter system. The w8a8 quantization (8-bit weights and activations) keeps memory footprint small while preserving much of the original model's capability. Expect slightly reduced precision compared to the full-precision version, but solid general instruction-following across a wide context window.

Capabilities

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

Reasoning & Logic

Moderate

Multimodal

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.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.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