Laguna S 2.1 FP8

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

Released July 2026262K context≈ 196,608 words

Laguna S 2.1 FP8 is an open-weight text model from poolside, quantized to FP8 precision — a format that trades a small amount of numerical fidelity for reduced memory footprint and faster inference. It operates over an exceptionally large context window of 262,144 tokens, meaning it can hold very long documents or conversations in view at once. Specific capability details beyond its architecture and access format are limited.

Capabilities

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

Long Context

Exceptional

Creative Writing

Laguna S 2.1 FP8

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

Released July 2026262K context≈ 196,608 words

Laguna S 2.1 FP8 is an open-weight text model from poolside, quantized to FP8 precision — a format that trades a small amount of numerical fidelity for reduced memory footprint and faster inference. It operates over an exceptionally large context window of 262,144 tokens, meaning it can hold very long documents or conversations in view at once. Specific capability details beyond its architecture and access format are limited.

Capabilities

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

Long Context

Exceptional

Creative Writing

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.FP8 PrecisionA data format that stores numbers using 8 bits instead of the standard 32 bits, significantly reducing memory requirements with minimal quality loss.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.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.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Use Case Fit

Glossary