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Qwen3.5-27B-FP8 GPU Requirements: VRAM & Cheapest GPU

Qwen3.5-27B-FP8 has about 27.8B 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.

27.8BParameters
15 GBMin VRAM
$0.65/hrCheapest
< 2 minDeploy
Qwen/Qwen3.5-27B-FP8
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27.8B paramsimage-text-to-textqwen3_52.0M downloads134 likesupdated Apr 24, 2026

To run Qwen3.5-27B-FP8 for inference at FP16, you need roughly 61 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
    2× RTX 5090 32GB
    Blackwell · GDDR7
    $0.86/hr$1.72/hr
  • 05
    1× GH200 96GB
    Grace Hopper · HBM3
    $1.88/hr$1.88/hr

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

VRAM required to run Qwen3.5-27B-FP8

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
FP1661 GB91 GB242 GB
INT830 GB45 GB121 GB
INT415 GB23 GB61 GB

Cheapest GPU to run Qwen3.5-27B-FP8 by precision

FP16
VRAM required61GB

Full precision. Best quality, highest memory.

Cheapest GPU
A100 80GB
Ampere · HBM2e
$0.82/hr
A100 80GB on Spheron
INT8
VRAM required30GB

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

Cheapest GPU
A100 80GB
Ampere · HBM2e
$0.82/hr
A100 80GB on Spheron
INT4
VRAM required15GB

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 Qwen3.5-27B-FP8

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 Qwen3.5-27B-FP8, 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 3.5 step by stepHands-on production setup, GPU configs, and benchmarks for Qwen3.5-27B-FP8.Read guide

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