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All estimates are approximations based on mathematical models and public specifications. Actual performance may vary. Do not make purchasing decisions based solely on these estimates.

Data sourced from Hugging Face, Ollama, and official model documentation. Model names and logos are trademarks of their respective owners.

© 2026 Will It Run AI — Fase Consulting Ibiza, S.L. (NIF: B57969656)

Home/embeddinggemma 300M/on Mac Studio M3 Ultra 96GB

Can it run?

Can Mac Studio M3 Ultra 96GB run embeddinggemma 300M?

CUsable

Runs well

Using Q6_K in Ollama

Capabilities:

Fit status

Runs well

Decode

584.8 tok/s

TTFT

350 ms

Safe context

88K

Memory

12.6 GB / 69.1 GB

Memory breakdown

Weights0.2 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom10.4 GB

Performance by workload

WorkloadGradeFitDecodeTTFTContext
Agentic CodingCRuns well584.8 tok/s482 ms175K
ChatCRuns well584.8 tok/s350 ms44K
CodingCRuns well584.8 tok/s350 ms88K
RAGCRuns well584.8 tok/s602 ms175K
ReasoningCRuns well584.8 tok/s391 ms88K

Quantization options

How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.1 GB
LowD29
Q3_K_S
3
0.1 GB
LowD29
NVFP4
4
0.2 GB
MediumD29
Q4_K_M
4
0.2 GB
MediumD29
Q5_K_M
5
0.2 GB
HighD29
Q6_K
6
0.2 GB
HighD29
Q8_0
8
0.3 GB
Very HighD29
F16Best for your GPU
16
0.6 GB
MaximumD30

Get started

HuggingFace
huggingface-cli download hf-ggml-org--embeddinggemma-300m-gguf
See all results for Mac Studio M3 Ultra 96GBSee all hardware for embeddinggemma 300M