phi 4 quantized.w4a16

Phi 4

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

Released March 202516K context≈ 12,288 words

A compact but capable text model that punches above its weight class for its size. The W4A16 quantization means weights are stored in 4-bit precision while activations run at 16-bit, striking a balance between memory efficiency and output quality. Expect slightly reduced fidelity compared to the full-precision Phi 4, but with a much smaller memory footprint that makes local deployment practical.

Capabilities

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

Multilingual

Moderate

Coding

phi 4 quantized.w4a16

Phi 4

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

Released March 202516K context≈ 12,288 words

A compact but capable text model that punches above its weight class for its size. The W4A16 quantization means weights are stored in 4-bit precision while activations run at 16-bit, striking a balance between memory efficiency and output quality. Expect slightly reduced fidelity compared to the full-precision Phi 4, but with a much smaller memory footprint that makes local deployment practical.

Capabilities

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

Multilingual

Moderate

Coding

Glossary

4-bit PrecisionA quantization level where model weights are stored using only 4 bits per value, significantly reducing model size at the cost of some accuracy.Bit PrecisionThe number of bits used to represent each number in a model; lower bit precision (like 3-bit) means smaller file size but potentially less accurate calculations.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.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.Memory EfficiencyHow well a model uses available RAM or GPU memory, allowing it to run on smaller or less expensive hardware.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.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.W4A16A quantization format where model weights are stored in 4-bit precision while calculations use 16-bit precision, balancing efficiency with accuracy.W4A16 QuantizationA specific quantization scheme where weights are stored in 4-bit precision while activations remain in 16-bit precision, balancing memory savings with accuracy.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary