Laguna S 2.1 INT4

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

Released July 2026262K context≈ 196,608 words

Laguna S 2.1 INT4 is a quantized open-weight text model from poolside, running at INT4 precision which reduces memory footprint at the cost of some numerical fidelity compared to full-precision variants. It handles an unusually large context window of 262,144 tokens, making it capable of processing very long documents in a single pass. The INT4 quantization makes it more accessible for deployment on consumer or edge hardware.

Capabilities

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

Long Context

Exceptional

Coding

Laguna S 2.1 INT4

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

Released July 2026262K context≈ 196,608 words

Laguna S 2.1 INT4 is a quantized open-weight text model from poolside, running at INT4 precision which reduces memory footprint at the cost of some numerical fidelity compared to full-precision variants. It handles an unusually large context window of 262,144 tokens, making it capable of processing very long documents in a single pass. The INT4 quantization makes it more accessible for deployment on consumer or edge hardware.

Capabilities

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

Long Context

Exceptional

Coding

Glossary

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.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.INT4 PrecisionA quantization method that represents model weights using only 4-bit integers instead of full-precision floating-point numbers, dramatically shrinking the model's memory footprint.INT4 QuantizationA compression technique that reduces a model's size and memory usage by storing weights as 4-bit integers instead of higher-precision numbers, making it faster and cheaper to run with minimal accuracy loss.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.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