text model · Granite · Windows
Can I run Granite 4.0 H Small on Nvidia GeForce RTX 5090 (32GB)?
Yes. Granite 4.0 H Small runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~20.3 GB of ~31 GB usable).
Runs at Q4_K_M using ~20.3 GB of ~31 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 5090 (32GB) leaves ~10.7 GB of headroom.
- Q4_K_M needed
- ~20.3 GB
- Usable on device
- ~31 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run granite4:32b-a9b-h llama-cli -hf ibm-granite/granite-4.0-h-small-GGUF:Q4_K_M lms get ibm-granite/granite-4.0-h-small-GGUF - Parameters
- 32B (MoE, 9B active)
- Q4_K_M size
- 18.14 GB
- Q8_0 size
- 31.91 GB
- Context
- 128k
- Ollama tag
- granite4:32b-a9b-h
- Memory
- 32 GB vram
- Usable for weights
- ~31 GB
- Power draw
- ~575 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Granite 4.0 H Small on other hardware
FAQ
Can Nvidia GeForce RTX 5090 (32GB) run Granite 4.0 H Small?
Yes. Granite 4.0 H Small runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~20.3 GB of ~31 GB usable).
How much memory does Granite 4.0 H Small need?
Nvidia GeForce RTX 5090 (32GB) has room to spare. At Q4_K_M the weights are ~18.14 GB; with KV cache and runtime overhead, budget ~20.3 GB at a 4k context. It is a Mixture-of-Experts model (32B total / 9B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Granite 4.0 H Small on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
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Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.