GLM 5.3 NVFP4

GLM

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

Released August 20261049K context≈ 786,432 words

GLM 5.3 NVFP4 is a text-in, text-out model from the GLM family, quantized to NVFP4 precision — a format optimized for NVIDIA hardware that trades some numerical fidelity for reduced memory footprint and faster inference. Its massive 1M-token context window is its most striking feature, allowing it to process very long documents or conversations in a single pass. Details about its specific reasoning or language capabilities beyond the context length are limited.

Capabilities

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

Long Context

Exceptional

Multilingual

GLM 5.3 NVFP4

GLM

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

Released August 20261049K context≈ 786,432 words

GLM 5.3 NVFP4 is a text-in, text-out model from the GLM family, quantized to NVFP4 precision — a format optimized for NVIDIA hardware that trades some numerical fidelity for reduced memory footprint and faster inference. Its massive 1M-token context window is its most striking feature, allowing it to process very long documents or conversations in a single pass. Details about its specific reasoning or language capabilities beyond the context length are limited.

Capabilities

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

Long Context

Exceptional

Multilingual

Glossary

Context LengthThe maximum amount of previous text a model can consider when generating its next output; longer context allows the model to maintain coherence over longer passages.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.NVFP4 PrecisionA low-precision numerical format that uses 4 bits per weight, developed by NVIDIA to compress models for efficient inference on consumer hardware.NVFP4 PrecisionA low-precision numerical format optimized by NVIDIA that uses fewer bits per number than standard formats, enabling efficient inference on NVIDIA GPUs while maintaining reasonable accuracy.Numerical FidelityThe accuracy and precision with which a model preserves mathematical calculations; lower fidelity means some precision is lost, often as a trade-off for smaller model size.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.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