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HelpingAI2 9B (9B parameters) requires approximately 8.3 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 10 GB of VRAM.
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | — |
Q3_K_S | 3 | 4.4 GB | Low | — |
NVFP4 | 4 | 5.0 GB | Medium | — |
Q4_K_M | 4 | 5.5 GB | Medium | — |
Q5_K_M | 5 | 6.5 GB | High | — |
Q6_K | 6 | 7.4 GB | High | — |
Q8_0 | 8 | 9.6 GB | Very High | — |
F16 | 16 | 18.5 GB | Maximum | — |
Hardware compatibility
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Memory breakdown
Frequently asked questions
HelpingAI2 9B (9B parameters) requires approximately 8.3 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc B570 10GB can run HelpingAI2 9B with a compatibility score of 51/100. It provides 10 GB of memory and achieves approximately 37.4 tokens per second.
The recommended quantization for HelpingAI2 9B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for HelpingAI2 9B: RTX 3080 Ti 12GB (score: 56/100), RTX 3080 12GB (score: 56/100), RTX 5070 12GB (score: 56/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, HelpingAI2 9B is well-suited for chat. It was designed with these use cases in mind.
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