DeepSeek V4 Flash 4bit

DeepSeek

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

Released April 20261049K context≈ 786,432 words

A quantized, memory-efficient version of DeepSeek's V4 Flash model, packaged by the MLX community for Apple Silicon hardware. The 4-bit quantization reduces memory footprint significantly, making it practical to run locally on Macs, though with some trade-off in precision compared to full-weight versions. It handles text-in, text-out tasks with a remarkably large context window of over one million tokens.

Capabilities

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

Long Context

Exceptional

Multilingual

DeepSeek V4 Flash 4bit

DeepSeek

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

Released April 20261049K context≈ 786,432 words

A quantized, memory-efficient version of DeepSeek's V4 Flash model, packaged by the MLX community for Apple Silicon hardware. The 4-bit quantization reduces memory footprint significantly, making it practical to run locally on Macs, though with some trade-off in precision compared to full-weight versions. It handles text-in, text-out tasks with a remarkably large context window of over one million tokens.

Capabilities

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

Long Context

Exceptional

Multilingual

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.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.MLXA machine learning framework optimized for running models efficiently on Apple Silicon chips.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-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Use Case Fit

Glossary