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embeddinggemma 300M (0.30000001192092896B parameters) requires approximately 2.1 GB of VRAM with Q6_K quantization. For the best balance of quality and speed, we recommend hardware with at least 3 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run embeddinggemma 300M on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "ggml-org/embeddinggemma-300M-GGUF" \
--hf-file "embeddinggemma-300M-GGUF-Q6_K.gguf" \
-c 4096 -ngl 99Quick specs
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Quantization options
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | — |
Q3_K_S | 3 | 0.1 GB | Low | — |
NVFP4 | 4 | 0.2 GB | Medium | — |
Q4_K_M | 4 | 0.2 GB | Medium | — |
Q5_K_M | 5 | 0.2 GB | High | — |
Q6_K | 6 | 0.2 GB | High | — |
Q8_0 | 8 | 0.3 GB | Very High | — |
F16 | 16 | 0.6 GB | Maximum | — |
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
embeddinggemma 300M (0.30000001192092896B parameters) requires approximately 2.1 GB of VRAM with Q6_K quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc A380 6GB can run embeddinggemma 300M with a compatibility score of 39/100. It provides 6 GB of memory and achieves approximately 4.2 tokens per second.
The recommended quantization for embeddinggemma 300M is Q6_K, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for embeddinggemma 300M: GTX 1650 4GB (score: 43/100), RTX 3050 Ti Laptop 4GB (score: 43/100), Intel Arc A370M 4GB (score: 41/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, embeddinggemma 300M is well-suited for chat. It was designed with these use cases in mind.
See also
