GTX 1660 Super 6GBBudget pick6 GB VRAM (+2)336 GB/s (+208)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$229 MSRP
HelpingAI 3B hindi i1 needs ~3.8 GB VRAM. GTX 1650 4GB has 4.0 GB. With Q4_K_M quantization, expect ~35 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
Tight fit
Decode
35.0 tok/s
TTFT
5536 ms
Safe context
26K
Memory
3.8 GB / 4.0 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 | 35.0 tok/s | 3020 ms | 26K |
| Coding | C | Tight fit | 35.0 tok/s | 5536 ms | 26K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 23.7 tok/s | 11862 ms | 26K |
| Reasoning | C | Tight fit | 35.0 tok/s | 6542 ms | 26K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 23.7 tok/s | 14827 ms |
How HelpingAI 3B hindi i1 (3B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C55 |
Q3_K_S | 3 | 1.5 GB | Low | C55 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 3B hindi i1 on your machine.
Run
lms load hf-mradermacher--helpingai-3b-hindi-i1-gguf && lms server startUpgrade options
GTX 1660 Super 6GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$229 MSRP
GTX 1060 6GBBest valueAdds memory headroom for longer context windows and future model growth.
~$249 MSRP
GTX 1660 Ti 6GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$279 MSRP
Yes, GTX 1650 4GB can run HelpingAI 3B hindi i1 with a C grade (Tight fit). Expected decode speed: 35.0 tok/s.
HelpingAI 3B hindi i1 (3B parameters) requires approximately 3.8 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 3B hindi i1 is Q4_K_M, which balances quality and memory efficiency.
On GTX 1650 4GB, HelpingAI 3B hindi i1 achieves approximately 35.0 tokens per second decode speed with a time-to-first-token of 5536ms using Q4_K_M quantization.
For coding workloads, HelpingAI 3B hindi i1 on GTX 1650 4GB receives a C grade with 35.0 tok/s and 26K context.
On GTX 1650 4GB, HelpingAI 3B hindi i1 can safely use up to 26K 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--helpingai-3b-hindi-i1-gguf-on-gtx-1650-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 26K |
| Medium |
| C54 |
Q4_K_MBest for your GPU | 4 | 1.8 GB | Medium | C54 |
Q5_K_M | 5 | 2.2 GB | High | F0 |
Q6_K | 6 | 2.5 GB | High | F0 |
Q8_0 | 8 | 3.2 GB | Very High | F0 |
F16 | 16 | 6.1 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.