Ingot 27B DFlash2

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

Released February 2026context N/A

Ingot 27B DFlash2 is a mid-sized open-weight text model from dendriteholdings, available in GGUF format for local deployment. At 27 billion parameters, it sits in a range that balances capability with hardware accessibility. Details about its specific strengths, training focus, or behavioral tendencies are limited beyond its architecture size and format availability.

Capabilities

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

Instruction Following

Moderate

Creative Writing

Moderate

Factual Knowledge

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Ingot 27B DFlash2

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

Released February 2026context N/A

Ingot 27B DFlash2 is a mid-sized open-weight text model from dendriteholdings, available in GGUF format for local deployment. At 27 billion parameters, it sits in a range that balances capability with hardware accessibility. Details about its specific strengths, training focus, or behavioral tendencies are limited beyond its architecture size and format availability.

Capabilities

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

Instruction Following

Moderate

Creative Writing

Moderate

Factual Knowledge

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

AccessibilityDesigning technology so people with disabilities can use it effectively.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.GGUFA file format for quantized models designed for efficient CPU and GPU inference with llama.cpp.GGUF FormatA file format designed for efficient storage and loading of large language and embedding models, optimized for fast inference on various hardware.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.