HelpingAI 15B i1 needs ~13.3 GB VRAM. RTX 5070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~29 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
1.3 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.9 GB host RAM)
Decode
28.7 tok/s
TTFT
6757 ms
Safe context
4K
Memory
13.3 GB / 12.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.3 GB host RAM) | 32.9 tok/s | 3206 ms | 4K |
| Coding | D | Very compromised (needs ~0.9 GB host RAM) | 28.7 tok/s | 6757 ms | 4K |
| Agentic Coding | F | Too heavy | 22.2 tok/s | 12659 ms | 4K |
| Reasoning | D | Very compromised | 28.7 tok/s | 7986 ms | 4K |
| RAG | F | Too heavy | 22.2 tok/s | 15824 ms |
How HelpingAI 15B i1 (15B params) fits at each quantization level on RTX 5070 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C52 |
Q3_K_S | 3 | 7.4 GB | Low | C51 |
NVFP4Best for your GPU |
Copy-paste commands to run HelpingAI 15B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server startUpgrade options
RTX 5060 Ti 16GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 2000 Ada 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$625 MSRP
Yes, RTX 5070 12GB can run HelpingAI 15B i1 with a D grade (Very compromised (needs ~0.9 GB host RAM)). Expected decode speed: 28.7 tok/s.
HelpingAI 15B i1 (15B parameters) requires approximately 13.3 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 5070 12GB, HelpingAI 15B i1 achieves approximately 28.7 tokens per second decode speed with a time-to-first-token of 6757ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on RTX 5070 12GB receives a D grade with 28.7 tok/s and 4K context.
On RTX 5070 12GB, 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-5070-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
8.4 GB |
| Medium |
| C51 |
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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.