GLM 5.2 W4AFP8

GLM

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

Released February 20261049K context≈ 786,432 words

GLM 5.2 W4AFP8 is a quantized open-weight text model with a remarkably large context window of over one million tokens, meaning it can hold and reason over very long documents in a single pass. The W4AFP8 designation indicates a mixed-precision quantization scheme, trading some numerical precision for reduced memory footprint — useful for running on constrained hardware. Concrete capability details beyond its architecture and context size are limited.

Capabilities

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

Multilingual

Moderate

Factual Knowledge

GLM 5.2 W4AFP8

GLM

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

Released February 20261049K context≈ 786,432 words

GLM 5.2 W4AFP8 is a quantized open-weight text model with a remarkably large context window of over one million tokens, meaning it can hold and reason over very long documents in a single pass. The W4AFP8 designation indicates a mixed-precision quantization scheme, trading some numerical precision for reduced memory footprint — useful for running on constrained hardware. Concrete capability details beyond its architecture and context size are limited.

Capabilities

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

Multilingual

Moderate

Factual Knowledge

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.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Mixed-Precision QuantizationUsing different numerical precisions (e.g., 8-bit, 4-bit) for different parts of a model to reduce memory and computation.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

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Glossary