How much VRAM does embeddinggemma 300M need?

embeddinggemma 300M (0.30000001192092896B parameters) requires approximately 4.8 GB of memory with Q6_K quantization.

What is the best quantization for embeddinggemma 300M?

The recommended quantization for embeddinggemma 300M is Q6_K, which balances quality and memory efficiency.

Can it run?

Yes, MacBook Pro M3 24GB can run embeddinggemma 300M with a C grade (Runs well). Expected decode speed: 68.3 tok/s.

CUsable

Runs well

Using Q6_K in Ollama

Capabilities:

Fit status

Runs well

Decode

68.3 tok/s

TTFT

2837 ms

Safe context

57K

Memory

4.8 GB / 17.3 GB

Weights0.2 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom2.6 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	C	Runs well	71.4 tok/s	3944 ms	114K
Chat	C	Runs well	71.4 tok/s	1479 ms	29K
Coding	C	Runs well	68.3 tok/s	2837 ms	57K
RAG	C	Runs well	71.4 tok/s	4930 ms	114K
Reasoning	C	Runs well	71.4 tok/s	3204 ms	57K

How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	D30
Q3_K_S	3	0.1 GB	Low	D30
NVFP4	4

HuggingFace

huggingface-cli download hf-ggml-org--embeddinggemma-300m-gguf

Upgrade options

MacBook Pro M4 32GBBudget pick

C90.7 tok/s decode

~$799 MSRP

Mac mini M4 32GBBest value

C90.7 tok/s decode

~$1,099 MSRP