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Model Comparison

ggml-org/embeddinggemma-300m-qat-q8_0-ggufvsnvidia/nvidia-nemotron-3-nano-4b-gguf

Side-by-side comparison of ggml-org/embeddinggemma-300m-qat-q8_0-gguf and nvidia/nvidia-nemotron-3-nano-4b-gguf: downloads, license, context length, tasks, and benchmarks.

ggml-org/embeddinggemma-300m-qat-q8_0-gguf

ggml-org · feature-extraction

# embeddinggemma-300m-qat-q8_0 GGUF Recommended way to run this model: ``sh llama-server -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF --embeddings ` Then the endpoint can be accessed at http://localhost:8080/embedding, for example using curl: `console curl --request POST \ --u…

nvidia/nvidia-nemotron-3-nano-4b-gguf

nvidia · text-generation

**Model Developer:** NVIDIA Corporation **Model Dates:** Dec 2025 \- Jan 2026 **Data Freshness:** September 2024 The pretraining data has a cutoff date of September 2024\.

Side-by-side Specifications

ggml-org/embeddinggemma-300m-qat-q8_0-ggufnvidia/nvidia-nemotron-3-nano-4b-gguf
Authorggml-orgnvidia
Pipeline Taskfeature-extractiontext-generation
Librarysentence-transformerstransformers
Downloads47,80626,805
Likes15118
LicenseUnknownUnknown
Context Length——
Created2025-09-042026-03-07
Last Modified2025-09-152026-03-16
Tags
sentence-transformersggufsentence-similarityfeature-extractionbase_model:google/embeddinggemma-300m-qat-q8_0-unquantizedbase_model:quantized:google/embeddinggemma-300m-qat-q8_0-unquantizedlicense:gemmaendpoints_compatibleregion:us
transformersggufnvidiapytorchtext-generationendataset:nvidia/Nemotron-CC-v2dataset:nvidia/Nemotron-Post-Training-Dataset-v2dataset:nvidia/Nemotron-Science-v1dataset:nvidia/Nemotron-Instruction-Following-Chat-v1

View full details: ggml-org/embeddinggemma-300m-qat-q8_0-gguf · nvidia/nvidia-nemotron-3-nano-4b-gguf