HelpingAI2 9B needs ~8.9 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~61 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
61.4 tok/s
TTFT
3154 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 | B | Runs well | 61.4 tok/s | 1721 ms | 62K |
| Coding | B | Runs well | 61.4 tok/s | 3154 ms | 62K |
| Agentic Coding | C | Tight fit | 61.4 tok/s | 4588 ms | 62K |
| Reasoning | B | Runs well | 61.4 tok/s | 3728 ms | 62K |
| RAG | C | Tight fit | 61.4 tok/s | 5735 ms | 62K |
How HelpingAI2 9B (9B params) fits at each quantization level on RTX 4080 Laptop 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 | 5.0 GB | Medium | C52 |
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 HelpingAI2 9B on your machine.
Run
lms load hf-bartowski--helpingai2-9b-gguf && lms server startYes, RTX 4080 Laptop 12GB can run HelpingAI2 9B with a B grade (Runs well). Expected decode speed: 61.4 tok/s.
HelpingAI2 9B (9B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 9B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4080 Laptop 12GB, HelpingAI2 9B achieves approximately 61.4 tokens per second decode speed with a time-to-first-token of 3154ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on RTX 4080 Laptop 12GB receives a B grade with 61.4 tok/s and 62K context.
On RTX 4080 Laptop 12GB, HelpingAI2 9B 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-bartowski--helpingai2-9b-gguf-on-rtx-4080-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: