HelpingAI 9B i1 needs ~8.2 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~16 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
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
15.9 tok/s
TTFT
12159 ms
Safe context
12K
Memory
8.2 GB / 8.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 22.6 tok/s | 4668 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 15.9 tok/s | 12159 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 12.4 tok/s | 22786 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 15.9 tok/s | 14369 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 12.4 tok/s | 28482 ms | 12K |
How HelpingAI 9B i1 (9B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C53 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4Best for your GPU | 4 | 5.0 GB | Medium | C52 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run HelpingAI 9B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-9b-i1-gguf && lms server startOpções de upgrade
RTX 3060 12GBOpção econômicaRaises estimated decode speed by about 129%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBMelhor custo-benefícioRaises estimated decode speed by about 218%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBUpgrade NVIDIARaises estimated decode speed by about 121%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, RTX 3050 8GB can run HelpingAI 9B i1 with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 15.9 tok/s.
HelpingAI 9B i1 (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 9B i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 8GB, HelpingAI 9B i1 achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12159ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B i1 on RTX 3050 8GB receives a C grade with 15.9 tok/s and 12K context.
On RTX 3050 8GB, HelpingAI 9B i1 can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai-9b-i1-gguf-on-rtx-3050-8gb" 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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