RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+72)
C
Raises estimated decode speed by about 47%.36.4 tok/s decode
Raises estimated decode speed by about 47%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
HelpingAI2 9B needs ~8.2 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~27 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
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
24.8 tok/s
TTFT
7806 ms
Safe context
12K
Memory
8.2 GB / 8.0 GB
This setup is broadly balanced for this model.
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.
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 | C | Runs with offload | 35.2 tok/s | 2997 ms | 12K |
| Coding | C | Runs with offload | 27.0 tok/s | 7182 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 19.2 tok/s | 14630 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 24.8 tok/s | 9226 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 19.2 tok/s | 18287 ms |
How HelpingAI2 9B (9B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C53 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4Best for your GPU |
Copy-paste commands to run HelpingAI2 9B on your machine.
Run
lms load hf-bartowski--helpingai2-9b-gguf && lms server startUpgrade options
RTX 3060 12GBBudget pickRaises estimated decode speed by about 47%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRaises estimated decode speed by about 104%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRaises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, RTX 3000 Ada Laptop 8GB can run HelpingAI2 9B with a C grade (Runs with offload). Expected decode speed: 27.0 tok/s.
HelpingAI2 9B (9B parameters) requires approximately 8.2 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 3000 Ada Laptop 8GB, HelpingAI2 9B achieves approximately 27.0 tokens per second decode speed with a time-to-first-token of 7182ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on RTX 3000 Ada Laptop 8GB receives a C grade with 27.0 tok/s and 12K context.
On RTX 3000 Ada Laptop 8GB, HelpingAI2 9B can safely use up to 12K 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-3000-ada-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 12K |
| 4 |
5.0 GB |
| Medium |
| C52 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.