Qwen3.5 9B Uncensored HauhauCS Aggressive needs ~8.6 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~30 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
30.0 tok/s
TTFT
6456 ms
Safe context
67K
Memory
8.6 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 | 30.0 tok/s | 3521 ms | 67K |
| Coding | C | Runs well | 30.0 tok/s | 6456 ms | 67K |
| Agentic Coding | C | Runs well | 30.0 tok/s | 9390 ms | 67K |
| Reasoning | C | Runs well | 30.0 tok/s | 7629 ms | 67K |
| RAG | C | Runs well | 30.0 tok/s | 11738 ms | 67K |
How Qwen3.5 9B Uncensored HauhauCS Aggressive (9B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C50 |
Q3_K_S | 3 | 4.4 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run Qwen3.5 9B Uncensored HauhauCS Aggressive on your machine.
Run
lms load hf-hauhaucs--qwen3-5-9b-uncensored-hauhaucs-aggressive && lms server startUpgrade options
Raises estimated decode speed by about 149%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 5070 Ti 16GBBest valueRaises estimated decode speed by about 248%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$749 MSRP
Yes, Intel Arc A730M 12GB can run Qwen3.5 9B Uncensored HauhauCS Aggressive with a C grade (Runs well). Expected decode speed: 30.0 tok/s.
Qwen3.5 9B Uncensored HauhauCS Aggressive (9B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 9B Uncensored HauhauCS Aggressive is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A730M 12GB, Qwen3.5 9B Uncensored HauhauCS Aggressive achieves approximately 30.0 tokens per second decode speed with a time-to-first-token of 6456ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 9B Uncensored HauhauCS Aggressive on Intel Arc A730M 12GB receives a C grade with 30.0 tok/s and 67K context.
On Intel Arc A730M 12GB, Qwen3.5 9B Uncensored HauhauCS Aggressive can safely use up to 67K 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-hauhaucs--qwen3-5-9b-uncensored-hauhaucs-aggressive-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:
5.0 GB |
| Medium |
| C52 |
Q4_K_M | 4 | 5.5 GB | Medium | C53 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C52 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
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.