HelpingAI2 9B needs ~9.3 GB VRAM. RTX 5070 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~105 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
Fit status
Runs well
Decode
104.5 tok/s
TTFT
1853 ms
Safe context
117K
Memory
9.3 GB / 16.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 104.5 tok/s | 1011 ms | 117K |
| Coding | C | Runs well | 104.5 tok/s | 1853 ms | 117K |
| Agentic Coding | B | Runs well | 104.5 tok/s | 2696 ms | 117K |
| Reasoning | C | Runs well | 104.5 tok/s | 2190 ms | 117K |
| RAG | B | Runs well | 104.5 tok/s | 3370 ms | 117K |
How HelpingAI2 9B (9B params) fits at each quantization level on RTX 5070 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C47 |
Q3_K_S | 3 | 4.4 GB | Low | C48 |
NVFP4 | 4 | 5.0 GB | Medium | C48 |
Q4_K_M | 4 | 5.5 GB | Medium | C49 |
Q5_K_M | 5 | 6.5 GB | High | C50 |
Q6_K | 6 | 7.4 GB | High | C51 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C51 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run HelpingAI2 9B on your machine.
Run
lms load hf-bartowski--helpingai2-9b-gguf && lms server startYes, RTX 5070 Ti 16GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 104.5 tok/s.
HelpingAI2 9B (9B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 9B is Q4_K_M, which balances quality and memory efficiency.
On RTX 5070 Ti 16GB, HelpingAI2 9B achieves approximately 104.5 tokens per second decode speed with a time-to-first-token of 1853ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on RTX 5070 Ti 16GB receives a C grade with 104.5 tok/s and 117K context.
On RTX 5070 Ti 16GB, HelpingAI2 9B can safely use up to 117K 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-bartowski--helpingai2-9b-gguf-on-rtx-5070-ti-16gb" 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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