internlm2 math plus 20b i1 needs ~14.2 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q3_K_S quantization, expect ~8 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
4.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.1 tok/s
TTFT
38083 ms
Safe context
4K
Memory
16.6 GB / 12.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.9 tok/s | 17813 ms | 4K |
| Coding | F | Too heavy | 5.1 tok/s | 38083 ms | 4K |
| Agentic Coding | F | Too heavy | 3.9 tok/s | 73096 ms | 4K |
| Reasoning | F | Too heavy | 5.1 tok/s | 45007 ms | 4K |
| RAG | F | Too heavy | 3.9 tok/s | 91370 ms | 4K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 7.8 GB | Low | C51 |
Q3_K_S | 3 | 9.8 GB | Low | F0 |
Copy-paste commands to run internlm2 math plus 20b i1 on your machine.
Run
lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server startUpgrade options
Intel Arc A770 16GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 167%.
~$349 MSRP
Intel Arc Pro B50 16GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 31%.
~$399 MSRP
Intel Arc Pro B60 24GBIntel upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$599 MSRP
Yes, Intel Arc A730M 12GB can run internlm2 math plus 20b i1 at Q3_K_S quantization (Very compromised (needs ~1.5 GB host RAM)). The recommended Q4_K_M requires 16.6 GB which exceeds available memory, but at Q3_K_S it needs only 14.2 GB. Expected decode speed: 8.2 tok/s.
internlm2 math plus 20b i1 (20B parameters) requires approximately 16.6 GB at Q4_K_M quantization. On Intel Arc A730M 12GB, it fits at Q3_K_S using 14.2 GB.
The recommended quantization is Q4_K_M, but on Intel Arc A730M 12GB the best fitting quantization is Q3_K_S, which uses 14.2 GB.
On Intel Arc A730M 12GB, internlm2 math plus 20b i1 achieves approximately 8.2 tokens per second decode speed with a time-to-first-token of 23700ms using Q3_K_S quantization.
For coding workloads, internlm2 math plus 20b i1 on Intel Arc A730M 12GB receives a F grade with 5.1 tok/s and 4K context.
On Intel Arc A730M 12GB, internlm2 math plus 20b i1 can safely use up to 4K tokens of context at Q3_K_S quantization. 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-mradermacher--internlm2-math-plus-20b-i1-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:
| 4 |
11.2 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 12.2 GB | Medium | F0 |
Q5_K_M | 5 | 14.4 GB | High | F0 |
Q6_K | 6 | 16.4 GB | High | F0 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.