It can: a phone’s vector index grows with the number of items it stores, while an embedding model’s weights are determined mainly by the model configuration. But the cited phone demo does not show an index larger than the model running it. Zvec reported about 24 MB of index files for 10,239 test images; Google reported about 191 MB of active RAM for EmbeddingGemma 2’s text-only weights on a Pixel 11 Pro. Those are different measurements from different deployments, not a head-to-head comparison.
What is a vector index, and how is it different from an AI model?
An embedding model turns content—such as text or an image—into a vector: a list of numbers that represents features of that content. A search system can compare those vectors to find items that are semantically similar. For example, a photo-search app might encode an image and a text query such as “receipt from a coffee shop,” then retrieve images whose vectors are close.
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The model generates embeddings; the vector index stores or organizes many embeddings so the app can find likely matches efficiently. They are separate components. The model’s weights are largely tied to its configuration, while the collection’s size depends on how many items have been indexed and how each vector is represented.
What the reported phone figures actually measure
There is no single “size” figure that makes all these numbers comparable. Model active RAM, raw vector data, index files on disk, index memory at runtime, and total application memory describe different things.
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| Figure | What it measures | Context |
|---|---|---|
| About 191 MB | Active RAM for EmbeddingGemma 2’s quantized text-only weights | Google’s October 6, 2026 launch-post claim for a Pixel 11 Pro configuration; not a universal model file size. Google |
| About 567 MB | Active RAM for the full multimodal EmbeddingGemma 2 model | Google’s launch-post claim for a Pixel 11 Pro configuration. Google |
| About 20 MB | Raw embedding data for the demo collection | Zvec’s PocketSearch test of 10,239 public test images. Zvec |
| About 24 MB | Index files | Zvec’s PocketSearch test of 10,239 public test images; this includes more than the raw embedding payload. Zvec |
| About 37 MB | Index memory delta | Zvec’s reported runtime figure for the same demo, not its on-disk file size. Zvec |
| About 875 MB | Overall running application memory | Zvec’s PocketSearch reference, including encoders/runtime, the Flutter app, image processing, and local index—not the index alone. Zvec |
Zvec says its prototype ran on a Xiaomi 14 Ultra with Android 15, using MobileCLIP encoders and Zvec—not EmbeddingGemma 2. Its test collection used public Unsplash Lite data and did not contain users’ private photos. The article reports end-to-end search around 117–131 ms and Zvec recall around 1 ms for that release-build test, attributing most measured search time to text-embedding computation. These are vendor-reported results for that device, build, and corpus, not results for phones or libraries generally.
So the cited demo’s index files were smaller than Google’s reported text-only active-RAM figure. The figures offer context, not proof that a particular phone’s index is larger or smaller than its model in a controlled test.
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Why an index can outgrow the model
A model has a configured set of weights. An index, by contrast, accumulates representations for the items in a collection. Its raw vector payload scales with the number of items, dimensions per vector, and bytes used for each value. Metadata and search structures add further storage; index design and implementation also matter.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteMicrosoft documents the raw vector-data calculation as number of documents × dimensions per vector × data-type size. A single-precision float uses four bytes per value. For a 768-dimensional float32 vector, that is 768 × 4 = 3,072 bytes before metadata or index overhead. At 10,000 items, raw vector values alone would occupy about 30.7 MB. This is arithmetic, not a phone benchmark. Microsoft’s discussion of HNSW overhead describes Azure AI Search internals and should not be treated as a mobile-device estimate. Microsoft Learn: Vector Index Limits
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As a collection gets larger, its raw vectors and the structures needed to search them can exceed the memory used by a particular embedding model configuration. That possibility does not establish that it happens for every app: vector dimensions, numeric representation, metadata, index design, and whether the comparison is storage on disk or active memory all affect the result.
What changes the model-side footprint?
Google describes EmbeddingGemma 2 as a 270-million-parameter text model with optional 170-million-parameter vision and 300-million-parameter audio encoders, for 740 million parameters in the full configuration. The model card lists a 768-dimensional output space and an 8,192-token context window. The full parameter count includes optional modality encoders; it does not mean a text-only configuration uses all of them. EmbeddingGemma 2 model card
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Google’s active-RAM figures are for particular quantized configurations and hardware. Parameter count multiplied by an assumed number of bytes per parameter is not a reliable estimate of total runtime memory: quantization, selected modalities, runtime software, temporary activations, and device hardware also matter. Google’s Gemma 4 documentation similarly notes that static-weight estimates do not include supporting software or context-window memory. Google AI for Developers: Gemma 4 model overview
How to reduce vector storage—and what it costs
Reducing the number of dimensions or using a smaller numeric representation can shrink raw vector payload. It may also affect retrieval quality, so evaluate the change on the intended content and search task rather than assuming a smaller vector is equivalent.
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EmbeddingGemma 2 supports truncation to 128, 256, 512, or 768 dimensions. Google DeepMind says truncating from 768 to 128 dimensions can reduce vector storage by up to 6×, with a quality trade-off; the model card provides benchmark results for the supported sizes. The outcome depends on the modality and task. EmbeddingGemma 2 model card
What to check before comparing two implementations
To decide whether an index is “bigger than the model,” first compare like with like and identify the measurement each figure represents. A useful comparison records:
- Corpus size, content type, and number of vectors.
- Embedding model and configuration, including modality.
- Vector dimensions and numeric precision.
- Raw vector bytes separately from metadata and index structures.
- On-disk storage separately from active or runtime memory.
- Retrieval quality or recall and latency for the same workload.
- Phone, operating system, runtime, and model-loading configuration.
Google’s authors describe local embedding generation as helping privacy, latency, and offline use. That is the authors’ product and technical characterization, not an independent privacy audit. Google
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