deepseek ai DeepSeek V4 Flash 8bit

DeepSeek

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

Released May 20251049K context≈ 786,432 words

A quantized, memory-efficient version of DeepSeek's V4 Flash model, packaged by the MLX community for Apple Silicon deployment. It runs text-in, text-out tasks with a massive 1M token context window, making it capable of handling very long documents in a single pass. The 8-bit quantization trades some precision for reduced memory footprint, a practical compromise for local inference.

Capabilities

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

Coding

Moderate

Factual Knowledge

deepseek ai DeepSeek V4 Flash 8bit

DeepSeek

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

Released May 20251049K context≈ 786,432 words

A quantized, memory-efficient version of DeepSeek's V4 Flash model, packaged by the MLX community for Apple Silicon deployment. It runs text-in, text-out tasks with a massive 1M token context window, making it capable of handling very long documents in a single pass. The 8-bit quantization trades some precision for reduced memory footprint, a practical compromise for local inference.

Capabilities

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

Coding

Moderate

Factual Knowledge

Glossary

8-bit QuantizationA specific quantization method that represents model weights using 8 bits instead of the standard 32 bits, significantly reducing memory requirements.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.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Local InferenceRunning an AI model directly on your own computer rather than sending data to a remote server, keeping data private and reducing latency.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.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