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Qwen3-32B GPU Requirements: VRAM & Cheapest GPU

Qwen3-32B has about 32.8B 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.

32.8BParameters
18 GBMin VRAM
$0.65/hrCheapest
< 2 minDeploy
32.8B paramstext-generationqwen34.2M downloads698 likesupdated Jul 26, 2025

To run Qwen3-32B for inference at FP16, you need roughly 71 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
    $0.91/hr$0.91/hr
  • 03
    1× H100 80GB
    Hopper · HBM3
    $1.49/hr$1.49/hr
  • 04
    1× GH200 96GB
    Grace Hopper · HBM3
    $1.88/hr$1.88/hr
  • 05
    2× L40S 48GB
    Ada Lovelace · GDDR6
    $0.96/hr$1.92/hr

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

VRAM required to run Qwen3-32B

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
FP1671 GB107 GB286 GB
INT836 GB54 GB143 GB
INT418 GB27 GB71 GB

Cheapest GPU to run Qwen3-32B by precision

FP16
VRAM required71GB

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 Qwen3-32B

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 Qwen3-32B, 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 Qwen3-32B.Read guide

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FAQ / 05

Qwen3-32B GPU questions