NVIDIA Nemotron 3.5 Lightning 30B A3B MLX 4bit

NVIDIA

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

Released October 2026262K context≈ 196,608 words

A compact-but-capable model that punches above its weight through sparse activation — only 3 billion parameters fire per token despite the 30B total parameter count, keeping inference lean. It handles very long contexts (up to 262K tokens), making it comfortable with large documents or extended conversations. The 4-bit quantization via MLX format means it's optimized for Apple Silicon hardware, trading some precision for dramatically reduced memory footprint.

Capabilities

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

Long Context

Exceptional

Instruction Following

NVIDIA Nemotron 3.5 Lightning 30B A3B MLX 4bit

NVIDIA

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

Released October 2026262K context≈ 196,608 words

A compact-but-capable model that punches above its weight through sparse activation — only 3 billion parameters fire per token despite the 30B total parameter count, keeping inference lean. It handles very long contexts (up to 262K tokens), making it comfortable with large documents or extended conversations. The 4-bit quantization via MLX format means it's optimized for Apple Silicon hardware, trading some precision for dramatically reduced memory footprint.

Capabilities

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

Long Context

Exceptional

Instruction Following

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.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.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.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.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.Sparse ActivationA technique where only a subset of a model's parameters are used for each input, reducing computational cost while maintaining performance.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Use Case Fit

Glossary