RTX 3060 12GBBudget pick12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.36.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
HelpingAI2 9B needs ~8.2 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~34 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
34.0 tok/s
TTFT
5697 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 49.8 tok/s | 2121 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 34.0 tok/s | 5697 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 26.1 tok/s | 10794 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 34.0 tok/s | 6733 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 26.1 tok/s |
How HelpingAI2 9B (9B params) fits at each quantization level on RTX 2070 Super 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 pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRaises estimated decode speed by about 49%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, RTX 2070 Super 8GB can run HelpingAI2 9B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 34.0 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 2070 Super 8GB, HelpingAI2 9B achieves approximately 34.0 tokens per second decode speed with a time-to-first-token of 5697ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on RTX 2070 Super 8GB receives a C grade with 34.0 tok/s and 12K context.
On RTX 2070 Super 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-2070-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 13492 ms |
| 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.