HelpingAI 9B 200k i1 needs ~8.1 GB but RTX 3050 Ti Laptop 4GB only has 4.0 GB. Try a smaller quantization or lighter model.
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
4.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.6 tok/s
TTFT
42274 ms
Safe context
4K
Memory
8.1 GB / 4.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 8.1 GB, but this setup only exposes 4.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.3 tok/s | 20028 ms | 4K |
| Coding | F | Too heavy | 4.6 tok/s | 42274 ms | 4K |
| Agentic Coding | F | Too heavy | 4.1 tok/s | 68821 ms | 4K |
| Reasoning | F | Too heavy | 4.6 tok/s | 49960 ms | 4K |
| RAG | F | Too heavy | 4.1 tok/s | 86026 ms | 4K |
How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | F0 |
Q3_K_S | 3 | 4.4 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX 3050 8GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 283%.
~$249 MSRP
RTX 5060 8GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 624%.
~$299 MSRP
RTX 3060 12GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$329 MSRP
No, HelpingAI 9B 200k i1 requires more memory than RTX 3050 Ti Laptop 4GB provides.
HelpingAI 9B 200k i1 (9B parameters) requires approximately 8.1 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 9B 200k i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, HelpingAI 9B 200k i1 achieves approximately 4.6 tokens per second decode speed with a time-to-first-token of 42274ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B 200k i1 on RTX 3050 Ti Laptop 4GB receives a F grade with 4.6 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, HelpingAI 9B 200k i1 can safely use up to 4K 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-9b-200k-i1-gguf-on-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
