Will my input fit?
Combine text, images, video and audio without accidentally exceeding the shared 8,192-token limit.
Updated for the October 6, 2026 release
Know what your EmbeddingGemma 2 setup needs before you build it.
For developers building RAG and multimodal search: calculate input limits, vector storage and the right embedding dimension in under a minute.
✓ No API key
✓ No file upload
✓ Runs in your browser
✓ Based on official model data
768dnative output
8,192shared input tokens
740Mfull model parameters
4input modalities
THE VALUE IN PLAIN ENGLISH
Answer three expensive questions before you deploy.
Instead of reading several model pages and building test scripts, enter your workload once and leave with a practical deployment starting point.
How much storage will I need?
Estimate vector bytes, metadata, index overhead, replicas and an approximate monthly storage charge.
Which setup should I start with?
Compare 128–768 dimensions, load only needed encoders and copy a matching Python configuration.
01 / INPUT BUDGET
Will this multimodal input fit?
Text, images, video frames and audio all draw from the same 8,192-token context. Change any input and the result updates locally.
Fits comfortably81%
6,620 / 8,192 tokens
02 / VECTOR STORAGE
How large will the index become?
Estimate raw vectors, metadata and index overhead. Managed databases may add replicas, backups and service-specific compute charges.
Estimated indexed footprint
952 MiB
Raw vectors488 MiB
Metadata244 MiB
Index overhead220 MiB
Monthly storage$0.25
03 / DIMENSION DECISION
Smaller vectors are not automatically better.
Google reports close-to-full quality at 256d for many text workloads, while multimodal quality falls more sharply at 128d. Use this table as a starting point, then test your own retrieval set.
04 / MODEL FOOTPRINT
Load only the encoders you need.
Parameters loaded270M
Approx. raw BF16 weights515 MiB
Weight estimate excludes framework, activations, caches and runtime overhead.
05 / METHODOLOGY
See exactly what the calculator includes.
Every estimate is transparent. Change the assumptions to match your workload instead of trusting a single vendor quote.
One shared context
Text tokens + images × visual tokens + sampled video frames × visual tokens + audio seconds × 25.
Total ≤ 8,192 tokens
Storage before provider fees
Vector values and metadata are calculated first, then multiplied by index overhead and replica count.
(vectors + metadata) × (1 + overhead) × replicas
Weights, not total VRAM
The model estimate uses selected encoder parameters × two bytes for BF16 weights. Runtime memory is additional.
parameters × 2 bytes
Last source check: October 12, 2026. Model behavior and provider pricing can change. Verify against the linked official documentation before production use.
06 / SAFE STARTER
Copy a configuration that matches your plan.
The generated snippet uses the selected dimension, re-normalizes truncated embeddings, and omits unused modality encoders.
07 / PRACTICAL ANSWERS
Before you commit to an index.
Which EmbeddingGemma 2 dimension should I use?
Start with 256d for storage-sensitive text retrieval, and 512d or 768d when code or multimodal recall matters more. Treat 128d as a text-first shortlist candidate, not a universal default. Benchmark on representative queries before re-indexing production data.
Why must truncated vectors be normalized?
Slicing a normalized 768d vector changes its length. Re-normalizing keeps cosine similarity meaningful. Google specifically recommends using truncate_dim together with normalize_embeddings=True.
Does this calculator call an API or upload my data?
No. All calculations run in your browser. The tool does not ask for files, prompts, credentials or an API key.
Is the model footprint estimate the same as required VRAM?
No. It estimates raw BF16 weights only. Real VRAM or RAM usage also includes the framework, activations, temporary tensors, caches, media processing and operating-system overhead.
Is the storage result my final vector database bill?
No. It is a planning estimate for vectors, metadata, index overhead and replicas. A provider may also charge for compute, operations, backups, network transfer and minimum capacity.
Can I mix text, images, video and audio in one input?
Yes. EmbeddingGemma 2 maps these modalities into one shared vector space, but all media and text inside one input share the same 8,192-token budget.
SOURCE DISCIPLINE