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// Gemma · Embedding

embeddinggemma-2: VRAM & local deployment

744M parameters. About 1.0 GB at 4-bit and 2.1 GB at BF16.

released 2026-09-14licence: Commercial OKmemory figures are estimates

01Specs

PublisherGoogle · google
Family / seriesGemma · embeddinggemma-2
TypeEmbedding (feature-extraction)
Parameters744M Source: counted from official safetensors weights
Released weightsBF16 · 1.5 GBdownload
LicenceApache-2.0 · Commercial OKPermissive licence (Apache-2.0 / MIT / CC-BY / OpenMDW): commercial use allowed.
Released2026-09-14 (Hugging Face repo created)

02How much VRAM it needs (estimate)

Embedding models are small; memory grows with batch size and sequence length.

PrecisionWeightsWeights + overheadSmallest GPUMac
BF16 / FP16 (original)1.5 GB 2.1 GB 8GB GPUMac 16GB
FP8 / INT8 / Q8_00.8 GB 1.4 GB 8GB GPUMac 16GB
4-bit (Q4_K_M / AWQ / GPTQ-Int4)0.5 GB 1.0 GB 8GB GPUMac 16GB

Bits per weight: BF16 16, FP8 8.5, 4-bit 4.85. “Fits” = total ≤ 90% of memory; Mac GPU can use ~2/3 of unified memory (≤36GB) or ~3/4 (larger). For MoE, all experts must be in memory, so size by total parameters. Method →

03How to run it

Only runtimes the publisher or the runtime’s official library names for this model.

ollama run embeddinggemma-2

Which inference engine to choose →

04Official repositories & quantised builds

1 official repo(s) from google.

05Other Gemma models

All 48 →
ModelParams4-bit totalReleased
diffusiongemma-26B-A4B-it25.8B / 4B18.3 GB2026-06-09
gemma-4-12B-it-qat-q4_0-unquantized12B9.3 GB2026-06-04
gemma-4-12B-it12B9.3 GB2026-05-23
gemma-4-E4B-it-qat-q4_0-unquantized7.94B5.9 GB2026-04-30
gemma-4-E2B-it-qat-q4_0-unquantized5.1B4.0 GB2026-04-29
gemma-4-26B-A4B-it-qat-q4_0-unquantized25.8B / 4B18.3 GB2026-04-29
gemma-4-31B-it-qat-q4_0-unquantized31.3B23.5 GB2026-04-28
gemma-4-26B-A4B-it25.8B / 4B18.3 GB2026-03-11

06Sources

Data checked 2026-10-06. Parameters, context and licence are copied from the official repository; memory figures are our estimates.

Data updated: Self-host open models(UTC+8)