HelpingAI 15B i1 needs ~12.5 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
8.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.5 tok/s
TTFT
78857 ms
Safe context
4K
Memory
12.5 GB / 4.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 12.5 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 | 2.5 tok/s | 43013 ms | 4K |
| Coding | F | Too heavy | 2.5 tok/s | 78857 ms | 4K |
| Agentic Coding | F | Too heavy | 2.5 tok/s | 114701 ms | 4K |
| Reasoning | F | Too heavy | 2.5 tok/s | 93194 ms | 4K |
| RAG | F | Too heavy | 2.5 tok/s | 143376 ms | 4K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | F0 |
Q3_K_S | 3 | 7.4 GB | Low | F0 |
NVFP4 | 4 | 8.4 GB | Medium | F0 |
Q4_K_M | 4 | 9.2 GB | Medium | F0 |
Q5_K_M | 5 | 10.8 GB | High | F0 |
Q6_K | 6 | 12.3 GB | High | F0 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Opciones de mejora
RTX 3060 12GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 528%.
~$329 MSRP
RTX 5060 Ti 16GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$449 MSRP
RTX 4060 Ti 16GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$499 MSRP
No, HelpingAI 15B i1 requires more memory than RTX 3050 Ti Laptop 4GB provides.
HelpingAI 15B i1 (15B parameters) requires approximately 12.5 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, HelpingAI 15B i1 achieves approximately 2.5 tokens per second decode speed with a time-to-first-token of 78857ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on RTX 3050 Ti Laptop 4GB receives a F grade with 2.5 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, HelpingAI 15B i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai-15b-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>
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