Can I Run SmolLM2 on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

Newer alternative. HuggingFace has not released a newer model in this library. If you are choosing today, SmolLM3 is a comparable current option. View SmolLM3 →

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 20 listopada 2024

Yes — comfortably

Yes, comfortably — SmolLM2 1.7B Instruct at Q8_0 needs about 4.2 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~11.8 GB spare and running at ~99.5 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~99.5 tok/s

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RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	16 GB
Memory bandwidth	448 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

SmolLM2 on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

Quant	Memory needed	Fits 16 GB?	Max context	Est. speed	Download
F16	5.8 GB	✓ Yes	8K	~67.5 tok/s	3.4 GB
Q8_0	4.2 GB	✓ Yes	8K	~99.5 tok/s	1.8 GB
Q6_K	3.8 GB	✓ Yes	8K	~113.4 tok/s	1.4 GB
Q5_K_M	3.6 GB	✓ Yes	8K	~121.2 tok/s	1.2 GB
Q4_K_M	3.4 GB	✓ Yes	8K	~129.6 tok/s	1 GB
Q3_K_M	3.1 GB	✓ Yes	8K	~146.8 tok/s	0.7 GB
Q2_K	3 GB	✓ Yes	8K	~158.3 tok/s	0.6 GB

Which SmolLM2 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
SmolLM2 1.7B Instruct	3.4 GB	✓ Fits	~129.6 tok/s
SmolLM2 360M Instruct	1.4 GB	✓ Fits	~294.5 tok/s

RTX 5060 Ti 16 GB desktop limitations

The cheapest current 16 GB card, but its 448 GB/s bandwidth caps generation speed well below a 5080 on the same model.

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
16 GB of VRAM on the NVIDIA GeForce RTX 5060 Ti 16GB at 448 GB/s.
32 GB of system RAM available for CPU offload when a model exceeds VRAM.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is computed from this model's published attention configuration.

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