HelpingAI 15B i1 needs ~12.9 GB but GTX 1070 Ti 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
4.2 tok/s
TTFT
45974 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 4.9 tok/s | 21479 ms | 4K |
| Coding | F | Too heavy | 4.2 tok/s | 45974 ms | 4K |
| Agentic Coding | F | Too heavy | 3.2 tok/s | 88511 ms | 4K |
| Reasoning | F | Too heavy | 4.2 tok/s | 54332 ms | 4K |
| RAG | F | Too heavy | 3.2 tok/s | 110639 ms | 4K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on GTX 1070 Ti 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 | 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 |
アップグレードオプション
RTX 3060 12GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 274%.
〜$329 MSRP
RTX 5060 Ti 16GBコスパ重視Makes 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アップグレードMakes 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 GTX 1070 Ti 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 GTX 1070 Ti 8GB, HelpingAI 15B i1 achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 45974ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on GTX 1070 Ti 8GB receives a F grade with 4.2 tok/s and 4K context.
On GTX 1070 Ti 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.
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-gtx-1070-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
