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Qwen3-VL-32B-Instruct GPU Requirements: VRAM & Cheapest GPU

Qwen3-VL-32B-Instruct has about 33.4B 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.

33.4BParameters
18 GBMin VRAM
$0.65/hrCheapest
< 2 minDeploy
Qwen/Qwen3-VL-32B-Instruct
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33.4B paramsimage-text-to-textqwen3_vl2.4M downloads202 likesupdated Oct 21, 2025

To run Qwen3-VL-32B-Instruct for inference at FP16, you need roughly 73 GB of VRAM. The cheapest fit on Spheron is A100 80GB at about $0.82/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
    $0.82/hr$0.82/hr
  • 02
    1× RTX PRO 6000 96GB
    Blackwell · GDDR7
    $1.32/hr$1.32/hr
  • 03
    1× GH200 96GB
    Grace Hopper · HBM3
    $1.88/hr$1.88/hr
  • 04
    2× L40S 48GB
    Ada Lovelace · GDDR6
    $0.96/hr$1.92/hr
  • 05
    1× H100 80GB
    Hopper · HBM3
    $2.01/hr$2.01/hr

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

VRAM required to run Qwen3-VL-32B-Instruct

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
FP1673 GB109 GB291 GB
INT836 GB55 GB145 GB
INT418 GB27 GB73 GB

Cheapest GPU to run Qwen3-VL-32B-Instruct by precision

FP16
VRAM required73GB

Full precision. Best quality, highest memory.

Cheapest GPU
A100 80GB
Ampere · HBM2e
$0.82/hr
A100 80GB on Spheron
INT8
VRAM required36GB

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

Cheapest GPU
A100 80GB
Ampere · HBM2e
$0.82/hr
A100 80GB on Spheron
INT4
VRAM required18GB

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

Cheapest GPU
RTX 4090 24GB
Ada Lovelace · GDDR6X
$0.65/hr
RTX 4090 24GB on Spheron

Inference vs fine-tuning Qwen3-VL-32B-Instruct

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 Qwen3-VL-32B-Instruct, 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 Qwen 3 step by stepHands-on production setup, GPU configs, and benchmarks for Qwen3-VL-32B-Instruct.Read guide

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