HelpingAI 15B i1 needs ~12.9 GB but RTX 4060 Laptop 8GB only has 8.0 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
4.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.7 tok/s
TTFT
33675 ms
Safe context
4K
Memory
12.9 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 12.9 GB, but this setup only exposes 8.0 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 | 6.7 tok/s | 15834 ms | 4K |
| Coding | F | Too heavy | 5.7 tok/s | 33675 ms | 4K |
| Agentic Coding | F | Too heavy | 4.4 tok/s | 64084 ms | 4K |
| Reasoning | F | Too heavy | 5.7 tok/s | 39797 ms | 4K |
| RAG | F | Too heavy | 4.4 tok/s | 80105 ms | 4K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on RTX 4060 Laptop 8GB (8.0 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 |
Upgrade options
RTX 3060 12GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 175%.
~$329 MSRP
RTX 5060 Ti 16GBBudget pickMakes 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.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeMakes 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.
~$499 MSRP
No, HelpingAI 15B i1 requires more memory than RTX 4060 Laptop 8GB provides.
HelpingAI 15B i1 (15B parameters) requires approximately 12.9 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 4060 Laptop 8GB, HelpingAI 15B i1 achieves approximately 5.7 tokens per second decode speed with a time-to-first-token of 33675ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on RTX 4060 Laptop 8GB receives a F grade with 5.7 tok/s and 4K context.
On RTX 4060 Laptop 8GB, 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.
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-4060-laptop-8gb" 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 | 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 |
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.
