RTX 5060 Ti 16GBBudget pick16 GB VRAM (+4)448 GB/s (+112)
D
Raises estimated decode speed by about 36%.5.7 tok/s decode
Raises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
embeddinggemma 300M needs ~2.4 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q6_K quantization, expect ~4 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
4.2 tok/s
TTFT
46095 ms
Safe context
1.5M
Memory
2.4 GB / 12.0 GB
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 4.2 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Runs well | 4.2 tok/s | 25143 ms | 772K |
| Coding | D | Runs well | 4.2 tok/s | 46095 ms | 1.5M |
| Agentic Coding | D | Runs well | 4.2 tok/s | 67048 ms | 3.1M |
| Reasoning | D | Runs well | 4.2 tok/s | 54476 ms | 1.5M |
| RAG | D | Runs well | 4.2 tok/s | 83810 ms | 3.1M |
How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | C46 |
Q3_K_S | 3 | 0.1 GB | Low | C46 |
NVFP4 | 4 |
Copy-paste commands to run embeddinggemma 300M on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "ggml-org/embeddinggemma-300M-GGUF" \
--hf-file "embeddinggemma-300M-GGUF-Q6_K.gguf" \
-c 4096 -ngl 99Upgrade options
RTX 5060 Ti 16GBBudget pickRaises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 Ti 16GBBest valueRaises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP
Yes, Intel Arc A730M 12GB can run embeddinggemma 300M with a D grade (Runs well). Expected decode speed: 4.2 tok/s.
embeddinggemma 300M (0.30000001192092896B parameters) requires approximately 2.4 GB of memory with Q6_K quantization.
The recommended quantization for embeddinggemma 300M is Q6_K, which balances quality and memory efficiency.
On Intel Arc A730M 12GB, embeddinggemma 300M achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46095ms using Q6_K quantization.
For coding workloads, embeddinggemma 300M on Intel Arc A730M 12GB receives a D grade with 4.2 tok/s and 1.5M context.
On Intel Arc A730M 12GB, embeddinggemma 300M can safely use up to 1.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-ggml-org--embeddinggemma-300m-gguf-on-arc-a730m-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.2 GB |
| Medium |
| C47 |
Q4_K_M | 4 | 0.2 GB | Medium | C47 |
Q5_K_M | 5 | 0.2 GB | High | C47 |
Q6_K | 6 | 0.2 GB | High | C47 |
Q8_0 | 8 | 0.3 GB | Very High | C47 |
F16Best for your GPU | 16 | 0.6 GB | Maximum | C47 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.