輸入會不會超限？
一起計算文字、圖片、影片與音訊，避免超過共享的 8,192 Token 上限。
已支援 2026 年 10 月 6 日版本
開始開發前，先算清楚 EmbeddingGemma 2 需要多少資源。
為 RAG 與多模態搜尋開發者準備：一分鐘計算輸入上限、向量儲存與合適維度。
✓ 不需要 API Key
✓ 不上傳檔案
✓ 在瀏覽器執行
✓ 依據官方模型資料
768dnative output
8,192shared input tokens
740Mfull model parameters
4input modalities
用白話說明價值
部署前，先回答三個可能讓你多花錢的問題。
不用翻閱多份文件或先寫測試程式，輸入一次工作量就能得到實用起點。
需要多少儲存？
估算向量、元資料、索引開銷、副本與每月費用。
應該從哪種設定開始？
比較 128–768 維並複製相符的 Python 設定。
01 / INPUT BUDGET
這次多模態輸入會不會超限？
文字、圖片、影片影格與音訊共用 8,192 Token 上限。
Fits comfortably81%
6,620 / 8,192 tokens
02 / VECTOR STORAGE
向量索引會占多少空間？
估算向量、元資料、索引開銷與副本。
Estimated indexed footprint
952 MiB
Raw vectors488 MiB
Metadata244 MiB
Index overhead220 MiB
Monthly storage$0.25
03 / DIMENSION DECISION
向量越小，不代表一定越好。
先比較官方品質資料與儲存節省，再用自己的資料測試。
04 / MODEL FOOTPRINT
只載入真正需要的編碼器。
Parameters loaded270M
Approx. raw BF16 weights515 MiB
Weight estimate excludes framework, activations, caches and runtime overhead.
05 / METHODOLOGY
每一項計算都能看懂。
所有假設都公開，可依照工作負載調整。
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
複製符合目前方案的設定。
程式碼會隨選擇的維度與模態更新。
07 / PRACTICAL ANSWERS
建立索引前，先看這些問題。
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