Unverified50% confidenceFactExact time
Embedding Gemma 2 has 740 million parameters, consisting of a 270M text encoder, a 170M vision encoder, and a 300M audio encoder, with runtime dynamic memory of approximately 191MB to 567MB; the 270M version requires a minimum of 0.5GB memory to run, and the full multimodal version can run locally with 1GB of memory.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/8/2026
First Seen
Valid until: 1/6/2027
Sources
Related Entities
Related Claims
UnverifiedEmbeddingGemma 2's 740M parameters are divided into a text component of 270M (backbone 130M, embedding layer 140M), a vision encoder of 170M, and an audio encoder of 300M, with all three encoders loadable on demand.73% similarUnverifiedEmbedding Gemma 2 outputs 768-dimensional vectors (truncatable to 512, 256, or 128 dimensions), has a context length of 8,192 tokens, supports over 100 languages, and is released under the Apache 2.0 license.73% similarPartially VerifiedAfter quantization, EmbeddingGemma 2 requires only approximately 191 MB of active memory for text-only weights on the Google Pixel 11 Pro, while the full multimodal model requires approximately 567 MB.73% similarUnverifiedMemory requirements are at least 37.6 GB for Q2_0, at least 39.2 GB for IQ2_XS, and at least 47 GB for IQ3_XXS, with the vision encoder occupying an additional 0.91 GB.72% similarUnverifiedQwen 3.8's 27-billion-parameter model is approximately 15.3 GB after 4-bit quantization, but actual memory usage can surge to over 17 GB after a conversation of several thousand characters, meaning 16 GB of VRAM is insufficient and 24 GB is needed for comfortable use.69% similar
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