gemma 4 E4B it Q4 K M layers

Gemma

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

Released July 2026context N/A

A compact, quantized version of Google's Gemma 4 model, packaged by meshllm in GGUF format for local deployment. The Q4_K_M quantization means it trades some precision for significantly reduced memory footprint, making it runnable on consumer hardware. Behavior closely mirrors the base Gemma 4 architecture, though quantization can introduce subtle degradation in complex reasoning tasks.

Capabilities

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

Long Context

Moderate

Reasoning & Logic

Moderate

Creative Writing

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

gemma 4 E4B it Q4 K M layers

Gemma

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

Released July 2026context N/A

A compact, quantized version of Google's Gemma 4 model, packaged by meshllm in GGUF format for local deployment. The Q4_K_M quantization means it trades some precision for significantly reduced memory footprint, making it runnable on consumer hardware. Behavior closely mirrors the base Gemma 4 architecture, though quantization can introduce subtle degradation in complex reasoning tasks.

Capabilities

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

Long Context

Moderate

Reasoning & Logic

Moderate

Creative Writing

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Complex ReasoningThe ability to work through multi-step problems, analyze nuanced information, and draw logical conclusions.GGUFA file format for quantized models designed for efficient CPU and GPU inference with llama.cpp.GGUF FormatA file format designed for efficient storage and loading of large language and embedding models, optimized for fast inference on various hardware.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.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.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning TasksProblems that require a model to think through multiple steps logically to arrive at an answer, rather than just pattern-matching.