GLM 5.2 NVFP4

GLM

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

Released June 20261049K context≈ 786,432 words

An NVFP4-quantized variant of Zhipu AI's GLM-5.2 model, compressed to 4-bit precision for efficient deployment on NVIDIA hardware. Retains the base model's 1M-token context window and general-purpose text capabilities while significantly reducing memory requirements. The quantization trade-off is minimal for most tasks, making this a practical choice for running a large-context model on constrained GPU setups.

Capabilities

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

Long Context

Strong

Coding

GLM 5.2 NVFP4

GLM

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

Released June 20261049K context≈ 786,432 words

An NVFP4-quantized variant of Zhipu AI's GLM-5.2 model, compressed to 4-bit precision for efficient deployment on NVIDIA hardware. Retains the base model's 1M-token context window and general-purpose text capabilities while significantly reducing memory requirements. The quantization trade-off is minimal for most tasks, making this a practical choice for running a large-context model on constrained GPU setups.

Capabilities

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

Long Context

Strong

Coding

Glossary

4-bit PrecisionA quantization level where model weights are stored using only 4 bits per value, significantly reducing model size at the cost of some accuracy.Base ModelA pretrained model that completes text patterns but hasn't been trained to follow instructions, serving as a starting point for customization through fine-tuning.Bit PrecisionThe number of bits used to represent each number in a model; lower bit precision (like 3-bit) means smaller file size but potentially less accurate calculations.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.General-PurposeDesigned to handle a wide variety of different tasks rather than being specialized for one specific domain.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.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.

Capabilities

Capabilities

Use Case Fit

Glossary