Granite 4.0 Small-H 32B-A9B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated August 15, 2026

Model libraryIBM Granite 4.0 → Granite 4.0 Small-H 32B-A9B

The largest Granite 4.0 build — 32B total, 9B active, on a hybrid mamba-2 architecture (the '-h' suffix). Also published as `granite4:small-h`. The mamba-2 hybrid keeps memory growth flatter than a pure transformer at long context, which is the point for document-heavy enterprise RAG. Around 19 GB at the default Q4_K_M, so a single 24GB card. Twelve languages including English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch and Chinese.

Granite 4.0 Small-H 32B-A9B needs about 20 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters32 Billion (9B active)
Context window128,000
ArchitectureHybrid Mamba-2 + Mixture-of-Experts
ProviderIBM
LicenceApache 2.0
Specified atQ4_K_M
System RAM32 GB
Record updated2026-08-15

Licence

Apache-2.0commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K10.5 GB11.3 GB~135 tok/s (est.)Fits comfortably
Q3_K_M13.6 GB14.4 GB~117 tok/s (est.)Fits comfortably
Q4_K_M19.3 GB20.1 GB~94 tok/s (est.)Fits comfortably
Q5_K_M22.7 GB23.5 GB~84 tok/s (est.)Tight fit
Q6_K26.2 GB27.0 GB~11 tok/s (est.)Offloads to system RAM (slow)
Q8_034.0 GB34.8 GB~9 tok/s (est.)Offloads to system RAM (slow)
F1664.0 GB64.8 GBWon't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Granite 4.0 Small-H 32B-A9B VRAM calculator.

Buy This HardwareAMD Radeon RX 7900 XTX 24GB — 24 GB VRAM · 355 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs Granite 4.0 Small-H 32B-A9B is the AMD Radeon RX 7900 XT (20 GB).

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AMD Radeon RX 7900 XT 20GB
20 GB VRAM · 315 W board power
2026 prices are volatile — check the current listing.
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How to Run Granite 4.0 Small-H 32B-A9B

Install Ollama, then run:

ollama run granite4:32b-a9b-h

Weights on Hugging Face: ibm-granite/granite-4.0-h-small.

Best for: enterprise, rag, tool calling, multilingual, on premise.

Can I Run Granite 4.0 Small-H 32B-A9B on My GPU?

Granite 4.0 Small-H 32B-A9B — Frequently Asked Questions

How much VRAM does Granite 4.0 Small-H 32B-A9B need?
About 20 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Granite 4.0 Small-H 32B-A9B run on an RTX 4090 (24 GB)?
Yes. Granite 4.0 Small-H 32B-A9B needs about 20 GB at Q4_K_M, inside a 24 GB card, at an estimated 94 tokens/sec.
How do I run Granite 4.0 Small-H 32B-A9B locally?
Install Ollama and run `ollama run granite4:32b-a9b-h`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

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