HelpingAI 9B i1 needs ~8.9 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~45 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
44.7 tok/s
TTFT
4333 ms
Safe context
62K
Memory
8.9 GB / 12.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 44.7 tok/s | 2364 ms | 62K |
| Coding | C | Runs well | 44.7 tok/s | 4333 ms | 62K |
| Agentic Coding | C | Tight fit | 44.7 tok/s | 6303 ms | 62K |
| Reasoning | C | Runs well | 44.7 tok/s | 5121 ms | 62K |
| RAG | C | Tight fit | 44.7 tok/s | 7879 ms | 62K |
How HelpingAI 9B i1 (9B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C49 |
Q3_K_S | 3 | 4.4 GB | Low | C51 |
NVFP4 | 4 | 5.0 GB | Medium | C51 |
Q4_K_M | 4 | 5.5 GB | Medium | C52 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C51 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run HelpingAI 9B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-9b-i1-gguf && lms server startYes, RTX 3500 Ada Laptop 12GB can run HelpingAI 9B i1 with a C grade (Runs well). Expected decode speed: 44.7 tok/s.
HelpingAI 9B i1 (9B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 9B i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 3500 Ada Laptop 12GB, HelpingAI 9B i1 achieves approximately 44.7 tokens per second decode speed with a time-to-first-token of 4333ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B i1 on RTX 3500 Ada Laptop 12GB receives a C grade with 44.7 tok/s and 62K context.
On RTX 3500 Ada Laptop 12GB, HelpingAI 9B i1 can safely use up to 62K 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-mradermacher--helpingai-9b-i1-gguf-on-rtx-3500-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: