Can I Run IBM Granite 4.0 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes, but it is tight
Yes, but it is tight — Granite 4.0 Small-H 32B-A9B at Q6_K needs about 28.9 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving only ~3.1 GB before the runtime starts swapping. Expect ~118.7 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: low · Recommended quantization: Q6_K · Estimated speed: ~118.7 tok/s
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RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
IBM Granite 4.0 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 32 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 | ✓ Yes | 16K | ~118.7 tok/s | 26.2 GB |
| Q5_K_M | 25.3 GB | ✓ Yes | 32K | ~130.2 tok/s | 22.7 GB |
| Q4_K_M | 22 GB | ✓ Yes | 32K | ~143.4 tok/s | 19.3 GB |
| Q3_K_M | 16.3 GB | ✓ Yes | 64K | ~172.8 tok/s | 13.6 GB |
| Q2_K | 13.2 GB | ✓ Yes | 64K | ~194.8 tok/s | 10.5 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 5090 desktop limitations
- 575 W board power — budget for a 1000 W+ PSU and the heat it puts into the room.
- Models larger than 32 GB must offload to system RAM, which costs roughly an order of magnitude in speed.
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.
- 32 GB of VRAM on the NVIDIA GeForce RTX 5090 at 1792 GB/s.
- 64 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 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Yes, but it is tight — Granite 4.0 Small-H 32B-A9B at Q6_K needs about 28.9 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving only ~3.1 GB before the runtime starts swapping. Expect ~118.7 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of IBM Granite 4.0 should I use on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Q6_K — it needs about 28.9 GB of the 32 GB available, downloads as roughly 26.2 GB, and runs at an estimated 118.7 tokens/sec with up to 16K of context.
What limits IBM Granite 4.0 on RTX 5090 Desktop (32 GB VRAM, 64 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 5090 Desktop (32 GB VRAM, 64 GB RAM)
- IBM Granite 4.1 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- IBM Granite 4.2 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- InternLM 3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- LFM2.5 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Llama 3.1 Family on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
IBM Granite 4.0 on GPUs
- IBM Granite 4.0 on NVIDIA GeForce RTX 5090
- IBM Granite 4.0 on NVIDIA GeForce RTX 5080
- IBM Granite 4.0 on NVIDIA GeForce RTX 5070 Ti
- IBM Granite 4.0 on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
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