gemma 4 26B A4B it FP8

gemma

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

Released April 2026context N/A26B params

A compact but capable text model that punches above its weight class for its parameter count. Running in FP8 quantization keeps memory footprint lean while preserving most of the full-precision behavior. Handles general reasoning and language tasks solidly, though as a text-only model it won't help with images or audio.

Capabilities

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

Instruction Following

Strong

Multilingual

Moderate

Tool Use

Use Case Fit

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

gemma 4 26B A4B it FP8

gemma

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

Released April 2026context N/A26B params

A compact but capable text model that punches above its weight class for its parameter count. Running in FP8 quantization keeps memory footprint lean while preserving most of the full-precision behavior. Handles general reasoning and language tasks solidly, though as a text-only model it won't help with images or audio.

Capabilities

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

Instruction Following

Strong

Multilingual

Moderate

Tool Use

Use Case Fit

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

Glossary

FP8 QuantizationA compression technique that reduces model size by representing weights using 8-bit floating-point numbers instead of higher precision, making it faster and more memory-efficient.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.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.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.Text-Only ModelA model that processes and produces only text input and output, without support for images, audio, or other data types.Text-Only ModelA language model that processes and generates only text, without support for images, audio, or other data types.