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 5060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~36 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
36.4 tok/s
TTFT
5321 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 | 50.6 tok/s | 2087 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 36.4 tok/s | 5321 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 28.5 tok/s | 9894 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 36.4 tok/s | 6289 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 28.5 tok/s | 12368 ms | 12K |
How HelpingAI2 9B (9B params) fits at each quantization level on RTX 5060 Ti 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 | 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 |
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 39%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA upgradeRaises estimated decode speed by about 112%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Yes, RTX 5060 Ti 8GB can run HelpingAI2 9B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 36.4 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 5060 Ti 8GB, HelpingAI2 9B achieves approximately 36.4 tokens per second decode speed with a time-to-first-token of 5321ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on RTX 5060 Ti 8GB receives a C grade with 36.4 tok/s and 12K context.
On RTX 5060 Ti 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-5060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: