DeepSeek V4 Flash 2bit DQ

DeepSeek

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

Released April 20261049K context≈ 786,432 words

A heavily compressed version of DeepSeek's V4 model, quantized down to 2-bit with dynamic quantization by the MLX community. It trades some precision for dramatically reduced memory footprint, making large-context processing more accessible on consumer hardware. Expect faster inference and lower resource usage, with potential quality degradation compared to higher-bit versions.

Capabilities

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

Long Context

Strong

Reasoning & Logic

DeepSeek V4 Flash 2bit DQ

DeepSeek

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

Released April 20261049K context≈ 786,432 words

A heavily compressed version of DeepSeek's V4 model, quantized down to 2-bit with dynamic quantization by the MLX community. It trades some precision for dramatically reduced memory footprint, making large-context processing more accessible on consumer hardware. Expect faster inference and lower resource usage, with potential quality degradation compared to higher-bit versions.

Capabilities

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

Long Context

Strong

Reasoning & Logic

Glossary

Dynamic QuantizationA quantization approach that adjusts precision levels during inference based on the input data, optimizing the balance between speed and accuracy on-the-fly.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.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.

Capabilities

Capabilities

Use Case Fit

Glossary