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granite-4.0-h-small GPU Requirements: VRAM & Cheapest GPU

granite-4.0-h-small has about 32.2B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.

32.2BParameters
18 GBMin VRAM
$0.61/hrCheapest
< 2 minDeploy
ibm-granite/granite-4.0-h-small
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32.2B paramstext-generationgranitemoehybrid613.4K downloads307 likesupdated Nov 3, 2025

To run granite-4.0-h-small for inference at FP16, you need roughly 70 GB of VRAM. The cheapest fit on Spheron is A100 80GB at about $0.79/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.

GB VRAM REQUIRED
FP16INFERENCEBATCH 1CTX 4k

Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.

RANKCONFIGURATIONPER GPUTOTAL $/HR
  • 01
    1× A100 80GBCHEAPEST
    Ampere · HBM2e
    $0.79/hr$0.79/hr
  • 02
    1× RTX PRO 6000 96GB
    Blackwell · GDDR7
    $0.86/hr$0.86/hr
  • 03
    2× L40S 48GB
    Ada Lovelace · GDDR6
    $0.61/hr$1.22/hr
  • 04
    1× H100 80GB
    Hopper · HBM3
    $1.43/hr$1.43/hr
  • 05
    1× H200 141GB
    Hopper · HBM3e
    $1.77/hr$1.77/hr

Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.

VRAM required to run granite-4.0-h-small

Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.

PrecisionInferenceLoRA fine-tuneFull fine-tune
FP1670 GB105 GB281 GB
INT835 GB53 GB140 GB
INT418 GB26 GB70 GB

Cheapest GPU to run granite-4.0-h-small by precision

FP16
VRAM required70GB

Full precision. Best quality, highest memory.

Cheapest GPU
A100 80GB
Ampere · HBM2e
$0.79/hr
A100 80GB on Spheron
INT8
VRAM required35GB

8-bit quantized. ~2x smaller, minimal quality loss.

Cheapest GPU
L40S 48GB
Ada Lovelace · GDDR6
$0.61/hr
L40S 48GB on Spheron
INT4
VRAM required18GB

4-bit quantized. ~4x smaller, runs on smaller GPUs.

Cheapest GPU
L40S 48GB
Ada Lovelace · GDDR6
$0.61/hr
L40S 48GB on Spheron

Inference vs fine-tuning granite-4.0-h-small

InferenceWeights + KV cache
LoRA fine-tune~1.5×+ low-rank adapter
Full fine-tune~4×+ gradients + optimizer state

Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For granite-4.0-h-small, an on-demand A100 80GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.

Deployment guideDeploy Granite 4 step by stepHands-on production setup, GPU configs, and benchmarks for granite-4.0-h-small.Read guide

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