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Qwen3-VL-235B-A22B-Instruct GPU Requirements: VRAM & Cheapest GPU

Qwen3-VL-235B-A22B-Instruct has about 236B 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.

236BParameters
128 GBMin VRAM
$1.64/hrCheapest
< 2 minDeploy
Qwen/Qwen3-VL-235B-A22B-Instruct
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236B paramsimage-text-to-textqwen3_vl_moe1.8M downloads391 likesupdated Nov 26, 2025

To run Qwen3-VL-235B-A22B-Instruct for inference at FP16, you need roughly 514 GB of VRAM. The cheapest fit on Spheron is 8x A100 80GB at about $6.56/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
    8× A100 80GBCHEAPEST
    Ampere · HBM2e
    $0.82/hr$6.56/hr
  • 02
    2× B300 288GB
    Blackwell Ultra · HBM3e
    $3.32/hr$6.64/hr
  • 03
    8× RTX PRO 6000 96GB
    Blackwell · GDDR7
    $0.91/hr$7.28/hr
  • 04
    4× B200 192GB
    Blackwell · HBM3e
    $2.69/hr$10.76/hr
  • 05
    8× H100 80GB
    Hopper · HBM3
    $1.49/hr$11.92/hr

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

VRAM required to run Qwen3-VL-235B-A22B-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
FP16514 GB771 GB2055 GB
INT8257 GB385 GB1028 GB
INT4128 GB193 GB514 GB

Cheapest GPU to run Qwen3-VL-235B-A22B-Instruct by precision

FP16
VRAM required514GB

Full precision. Best quality, highest memory.

Cheapest GPU
8x A100 80GB
Ampere · HBM2e
$6.56/hr · $0.82/hr/gpu
8x A100 80GB on Spheron
INT8
VRAM required257GB

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

Cheapest GPU
4x A100 80GB
Ampere · HBM2e
$3.28/hr · $0.82/hr/gpu
4x A100 80GB on Spheron
INT4
VRAM required128GB

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

Cheapest GPU
2x A100 80GB
Ampere · HBM2e
$1.64/hr · $0.82/hr/gpu
2x A100 80GB on Spheron

Inference vs fine-tuning Qwen3-VL-235B-A22B-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-235B-A22B-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-235B-A22B-Instruct.Read guide

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