Can I Run IBM Granite 4.0 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated August 15, 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
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
Can I run IBM Granite 4.0 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
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.
Which quantization of IBM Granite 4.0 should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q2_K — it needs about 13.2 GB of the 16 GB available, downloads as roughly 10.5 GB, and runs at an estimated 130.9 tokens/sec with up to 16K of context.
What limits IBM Granite 4.0 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
IBM Granite 4.0 on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | IBM Granite 4.0 model page | Check your hardware