Can I Run IBM Granite 4.0 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026

Yes

Yes — Granite 4.0 Small-H 32B-A9B at Q2_K needs about 13.2 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~2.8 GB spare), at ~130.9 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~130.9 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

IBM Granite 4.0 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	66.6 GB	✗ No	—	—	64 GB
Q8_0	36.6 GB	✗ No	—	—	34 GB
Q6_K	28.9 GB	✗ No	—	—	26.2 GB
Q5_K_M	25.3 GB	✗ No	—	—	22.7 GB
Q4_K_M	22 GB	✗ No	—	—	19.3 GB
Q3_K_M	16.3 GB	✗ No	—	—	13.6 GB
Q2_K	13.2 GB	✓ Yes	16K	~130.9 tok/s	10.5 GB

What to watch out for

Q2_K is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
1 larger variant of IBM Granite 4.0 does not fit and would need CPU offload or different hardware.
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.

FAQ