gpt oss 120b MXFP4 Q8

gpt

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

Released August 2025131K context≈ 98,304 words120b params

A heavily quantized variant of a large GPT-class model, optimized for Apple Silicon via the MLX framework. The MXFP4 Q8 mixed-precision quantization keeps memory footprint manageable while preserving reasonable output quality, though some precision loss is expected compared to full-precision runs. It behaves like a capable generalist text model that trades a bit of fidelity for local deployability.

Capabilities

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

Multimodal

Moderate

Factual Knowledge

gpt oss 120b MXFP4 Q8

gpt

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

Released August 2025131K context≈ 98,304 words120b params

A heavily quantized variant of a large GPT-class model, optimized for Apple Silicon via the MLX framework. The MXFP4 Q8 mixed-precision quantization keeps memory footprint manageable while preserving reasonable output quality, though some precision loss is expected compared to full-precision runs. It behaves like a capable generalist text model that trades a bit of fidelity for local deployability.

Capabilities

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

Multimodal

Moderate

Factual Knowledge

Glossary

Apple SiliconApple's custom-designed processors (like M1, M2, M3) optimized for running machine learning models on Mac computers.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.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 FrameworkA machine learning framework specifically designed for running AI models efficiently on Apple Silicon hardware.MXFP4A low-precision floating-point format (4-bit) designed for efficient neural network computation while maintaining reasonable accuracy.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.Precision LossThe reduction in numerical accuracy that occurs when a model is compressed, which can slightly degrade performance on complex reasoning tasks while remaining acceptable for most everyday uses.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

Capabilities

Use Case Fit

Glossary