Can I Run BitNet b1.58 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 1 marca 2024

Yes — comfortably

Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving ~10.8 GB spare and running at ~129 tok/s (estimated).

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~129 tok/s

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RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	16 GB
Memory bandwidth	960 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

BitNet b1.58 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

Quant	Memory needed	Fits 16 GB?	Max context	Est. speed	Download
F16	8.3 GB	✓ Yes	—	~82.8 tok/s	6.6 GB
Q8_0	5.2 GB	✓ Yes	—	~129 tok/s	3.5 GB
Q6_K	4.4 GB	✓ Yes	—	~150.8 tok/s	2.7 GB
Q5_K_M	4 GB	✓ Yes	—	~163.4 tok/s	2.4 GB
Q4_K_M	3.7 GB	✓ Yes	—	~177.4 tok/s	2 GB
Q3_K_M	3.1 GB	✓ Yes	—	~207.6 tok/s	1.4 GB
Q2_K	2.8 GB	✓ Yes	—	~229 tok/s	1.1 GB

What to watch out for

This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.

RTX 5080 desktop limitations

16 GB VRAM is the binding constraint, not compute — a slower 24 GB card runs strictly more models.

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 5080 at 960 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 inferred from the parameter count because this model does not publish its full attention configuration.

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