Laguna S 2.1 NVFP4

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

Released July 2026262K context≈ 196,608 words

Laguna S 2.1 NVFP4 is an open-weight text model from poolside, quantized to NVFP4 precision — a format that trades a small amount of numerical fidelity for significantly reduced memory footprint and faster inference. It carries a very large context window of 262K tokens, meaning it can hold and reason over long documents in a single pass. Specific capability details beyond its architecture and quantization profile are limited.

Capabilities

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

Long Context

Exceptional

Coding

Laguna S 2.1 NVFP4

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

Released July 2026262K context≈ 196,608 words

Laguna S 2.1 NVFP4 is an open-weight text model from poolside, quantized to NVFP4 precision — a format that trades a small amount of numerical fidelity for significantly reduced memory footprint and faster inference. It carries a very large context window of 262K tokens, meaning it can hold and reason over long documents in a single pass. Specific capability details beyond its architecture and quantization profile are limited.

Capabilities

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

Long Context

Exceptional

Coding

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.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.NVFP4 PrecisionA low-precision numerical format that uses 4 bits per weight, developed by NVIDIA to compress models for efficient inference on consumer hardware.NVFP4 PrecisionA low-precision numerical format optimized by NVIDIA that uses fewer bits per number than standard formats, enabling efficient inference on NVIDIA GPUs while maintaining reasonable accuracy.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.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.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