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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 GTX 1070 8GB

Can it run?

Can GTX 1070 8GB run embeddinggemma 300M?

CUsable

Runs well

Using Q6_K in Ollama

Capabilities:

Fit status

Runs well

Decode

158.6 tok/s

TTFT

1221 ms

Safe context

42K

Memory

3.0 GB / 8.0 GB

Memory breakdown

Weights0.2 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom0.8 GB

Performance by workload

WorkloadGradeFitDecodeTTFTContext
Agentic CodingCRuns well158.6 tok/s1776 ms84K
ChatCRuns well158.6 tok/s666 ms21K
CodingCRuns well158.6 tok/s1221 ms42K
RAGCRuns well158.6 tok/s2220 ms84K
ReasoningCRuns well158.6 tok/s1443 ms42K

Quantization options

How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.1 GB
LowD30
Q3_K_S
3
0.1 GB
LowD30
NVFP4
4
0.2 GB
MediumD30
Q4_K_M
4
0.2 GB
MediumD30
Q5_K_M
5
0.2 GB
HighD30
Q6_K
6
0.2 GB
HighD30
Q8_0
8
0.3 GB
Very HighD30
F16Best for your GPU
16
0.6 GB
MaximumD31

Get started

HuggingFace
huggingface-cli download hf-ggml-org--embeddinggemma-300m-gguf
See all results for GTX 1070 8GBSee all hardware for embeddinggemma 300M