RTX 3050 8GBBudget pick8 GB VRAM (+2)
C
Adds memory headroom for longer context windows and future model growth.33.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
HelpingAI2 6B i1 needs ~5.9 GB VRAM. GTX 1660 Super 6GB has 6.0 GB. With Q4_K_M quantization, expect ~51 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 with offload
Decode
50.5 tok/s
TTFT
3834 ms
Safe context
19K
Memory
5.9 GB / 6.0 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 | Tight fit | 50.5 tok/s | 2091 ms | 19K |
| Coding | C | Runs with offload | 50.5 tok/s | 3834 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.3 GB host RAM) | 30.2 tok/s | 9321 ms | 19K |
| Reasoning | C | Runs with offload | 50.5 tok/s | 4531 ms | 19K |
| RAG | D | Very compromised (needs ~0.3 GB host RAM) | 30.2 tok/s | 11651 ms |
How HelpingAI2 6B i1 (6B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C54 |
Q3_K_S | 3 | 2.9 GB | Low | C53 |
NVFP4Best for your GPU |
Copy-paste commands to run HelpingAI2 6B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-6b-i1-gguf && lms server startUpgrade options
RTX 3050 8GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$249 MSRP
RTX 5060 8GBBest valueRaises estimated decode speed by about 48%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$299 MSRP
Yes, GTX 1660 Super 6GB can run HelpingAI2 6B i1 with a C grade (Runs with offload). Expected decode speed: 50.5 tok/s.
HelpingAI2 6B i1 (6B parameters) requires approximately 5.9 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 6B i1 is Q4_K_M, which balances quality and memory efficiency.
On GTX 1660 Super 6GB, HelpingAI2 6B i1 achieves approximately 50.5 tokens per second decode speed with a time-to-first-token of 3834ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 6B i1 on GTX 1660 Super 6GB receives a C grade with 50.5 tok/s and 19K context.
On GTX 1660 Super 6GB, HelpingAI2 6B i1 can safely use up to 19K 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--helpingai2-6b-i1-gguf-on-gtx-1660-super-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 19K |
| 4 |
3.4 GB |
| Medium |
| C53 |
Q4_K_M | 4 | 3.7 GB | Medium | F0 |
Q5_K_M | 5 | 4.3 GB | High | F0 |
Q6_K | 6 | 4.9 GB | High | F0 |
Q8_0 | 8 | 6.4 GB | Very High | F0 |
F16 | 16 | 12.3 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.