GLM OCR 4bit

GLM

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

Released February 2026context N/A

A quantized text model running in MLX format, optimized for Apple Silicon efficiency. The 4-bit quantization means it trades some precision for a significantly smaller memory footprint, making it practical to run locally on Macs. Its behavior mirrors the GLM family's general language capabilities, though quantization can introduce subtle quality trade-offs compared to full-precision versions.

Capabilities

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

Long Context

Moderate

Factual Knowledge

Moderate

Multilingual

Use Case Fit

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

GLM OCR 4bit

GLM

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

Released February 2026context N/A

A quantized text model running in MLX format, optimized for Apple Silicon efficiency. The 4-bit quantization means it trades some precision for a significantly smaller memory footprint, making it practical to run locally on Macs. Its behavior mirrors the GLM family's general language capabilities, though quantization can introduce subtle quality trade-offs compared to full-precision versions.

Capabilities

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

Long Context

Moderate

Factual Knowledge

Moderate

Multilingual

Use Case Fit

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

Glossary

4-bit QuantizationA specific type of quantization that represents model weights using only 4 bits instead of the original 32 bits, enabling very efficient inference on consumer hardware.Apple SiliconApple's custom-designed processors (like M1, M2, M3) optimized for running machine learning models on Mac computers.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.MLXA machine learning framework optimized for running models efficiently on Apple Silicon chips.MLX FormatA model format designed specifically for efficient inference on Apple Silicon devices, optimized for the MLX machine learning framework.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.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.

Capabilities

Use Case Fit

Capabilities

Use Case Fit

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Glossary