Huggingfacetb Smollm2 135M speed on NVIDIA RTX 6000 Ada and quantization-level VRAM fit.
NVIDIA RTX 6000 Ada meets the minimum VRAM requirement for Q4 inference of Huggingfacetb Smollm2 135M. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
NVIDIA RTX 6000 Ada can run Huggingfacetb Smollm2 135M with Q4 quantization. At approximately 214 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 47GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 1GB | 48GB | 214.19 tok/s | ✅ Fits comfortably |
| Q8 | 1GB | 48GB | 149.93 tok/s | ✅ Fits comfortably |
| FP16 | 1GB | 48GB | 81.39 tok/s | ✅ Fits comfortably |
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NVIDIA RTX 6000 Ada can run Huggingfacetb Smollm2 135M at Q4 with an estimated 214 tok/s.
Q4 inference is estimated to need about 1GB VRAM on this page, while NVIDIA RTX 6000 Ada has 48GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.