BGE Large EN v1.5 needs ~4.1 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With F16 quantization, expect ~5 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
4.7 tok/s
TTFT
41279 ms
Safe context
512
Memory
4.6 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 4.7 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 | A | Runs well | 4.7 tok/s | 22516 ms | 512 |
| Coding | A | Runs well | 4.7 tok/s | 41279 ms | 512 |
| Agentic Coding | A | Runs well | 4.7 tok/s | 60043 ms | 512 |
| Reasoning | A | Runs well | 4.7 tok/s | 48785 ms | 512 |
| RAG | A | Runs well | 4.7 tok/s | 75053 ms | 512 |
How BGE Large EN v1.5 (0.33500000834465027B 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 | A78 |
Q3_K_S | 3 | 0.2 GB | Low | A78 |
NVFP4 | 4 |
Copy-paste commands to run BGE Large EN v1.5 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "BAAI/bge-large-en-v1.5" \
--hf-file "bge-large-en-v1.5-F16.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 32.2 tok/s | |
| Qwen 3 14B | 14B | A | 12.4 tok/s |
Yes, Intel Arc A730M 12GB can run BGE Large EN v1.5 with a A grade (Runs well). Expected decode speed: 4.7 tok/s.
BGE Large EN v1.5 (0.33500000834465027B parameters) requires approximately 4.1 GB of memory with F16 quantization.
The recommended quantization for BGE Large EN v1.5 is F16, which balances quality and memory efficiency.
On Intel Arc A730M 12GB, BGE Large EN v1.5 achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41279ms using F16 quantization.
For coding workloads, BGE Large EN v1.5 on Intel Arc A730M 12GB receives a A grade with 4.7 tok/s and 512 context.
On Intel Arc A730M 12GB, BGE Large EN v1.5 can safely use up to 512 tokens of context. The model's official context limit is 512, 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/bge-large-en-v1.5-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 |
| A78 |
Q4_K_M | 4 | 0.2 GB | Medium | A78 |
Q5_K_M | 5 | 0.2 GB | High | A78 |
Q6_K | 6 | 0.3 GB | High | A78 |
Q8_0 | 8 | 0.4 GB | Very High | A78 |
F16Best for your GPU | 16 | 0.7 GB | Maximum | A78 |
| Qwen 3.5 4B | 4B | S | 56 tok/s |
| Qwen 3 8B | 8B | S | 36.3 tok/s |
 Phi-4 Mini Reasoning 4B | 3.8B | S | 53.2 tok/s |
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.