MacBook Pro M3 Pro 18GBBudget pick
18 GB Unified (+6)
C
This setup is broadly balanced for this model.28 tok/s decode
~$1,999 MSRP
Gemmasutra Mini 2B v1 needs ~3.6 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~28 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
28.0 tok/s
TTFT
6914 ms
Safe context
593K
Memory
3.6 GB / 12.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
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.
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 | C | Runs well | 28.0 tok/s | 3771 ms | 593K |
| Coding | C | Runs well | 28.0 tok/s | 6914 ms | 593K |
| Agentic Coding | C | Runs well | 28.0 tok/s | 10057 ms | 593K |
| Reasoning | C | Runs well | 28.0 tok/s | 8171 ms | 593K |
| RAG | C | Runs well | 28.0 tok/s | 12571 ms | 593K |
How Gemmasutra Mini 2B v1 (2B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.8 GB | Low | C47 |
Q3_K_S | 3 | 1.0 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run Gemmasutra Mini 2B v1 on your machine.
Run
lms load hf-thedrummer--gemmasutra-mini-2b-v1-gguf && lms server startUpgrade options
Yes, Intel Arc A730M 12GB can run Gemmasutra Mini 2B v1 with a C grade (Runs well). Expected decode speed: 28.0 tok/s.
Gemmasutra Mini 2B v1 (2B parameters) requires approximately 3.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemmasutra Mini 2B v1 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A730M 12GB, Gemmasutra Mini 2B v1 achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q4_K_M quantization.
For coding workloads, Gemmasutra Mini 2B v1 on Intel Arc A730M 12GB receives a C grade with 28.0 tok/s and 593K context.
On Intel Arc A730M 12GB, Gemmasutra Mini 2B v1 can safely use up to 593K 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-thedrummer--gemmasutra-mini-2b-v1-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:
1.1 GB |
| Medium |
| C47 |
Q4_K_M | 4 | 1.2 GB | Medium | C47 |
Q5_K_M | 5 | 1.4 GB | High | C48 |
Q6_K | 6 | 1.6 GB | High | C48 |
Q8_0 | 8 | 2.1 GB | Very High | C48 |
F16Best for your GPU | 16 | 4.1 GB | Maximum | C51 |
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.
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.