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ThinkingCap-Qwen3.6-27B GPU Requirements: VRAM & Cheapest GPU

ThinkingCap-Qwen3.6-27B has about 27.4B 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.

27.4BParameters
15 GBMin VRAM
$0.58/hrCheapest
< 2 minDeploy
bottlecapai/ThinkingCap-Qwen3.6-27B
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27.4B paramsimage-text-to-textqwen3_546 downloads91 likesupdated Jul 7, 2026

To run ThinkingCap-Qwen3.6-27B for inference at FP16, you need roughly 60 GB of VRAM. The cheapest fit on Spheron is 2x L40S 48GB at about $1.22/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
    2× L40S 48GBCHEAPEST
    Ada Lovelace · GDDR6
    $0.61/hr$1.22/hr
  • 02
    1× RTX PRO 6000 96GB
    Blackwell · GDDR7
    $1.32/hr$1.32/hr
  • 03
    2× RTX 5090 32GB
    Blackwell · GDDR7
    $0.68/hr$1.36/hr
  • 04
    1× A100 80GB
    Ampere · HBM2e
    $1.80/hr$1.80/hr
  • 05
    1× H200 141GB
    Hopper · HBM3e
    $1.82/hr$1.82/hr

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

VRAM required to run ThinkingCap-Qwen3.6-27B

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
FP1660 GB89 GB239 GB
INT830 GB45 GB119 GB
INT415 GB22 GB60 GB

Cheapest GPU to run ThinkingCap-Qwen3.6-27B by precision

FP16
VRAM required60GB

Full precision. Best quality, highest memory.

Cheapest GPU
2x L40S 48GB
Ada Lovelace · GDDR6
$1.22/hr · $0.61/hr/gpu
2x L40S 48GB on Spheron
INT8
VRAM required30GB

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

Cheapest GPU
L40S 48GB
Ada Lovelace · GDDR6
$0.61/hr
L40S 48GB on Spheron
INT4
VRAM required15GB

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

Cheapest GPU
RTX 4090 24GB
Ada Lovelace · GDDR6X
$0.58/hr
RTX 4090 24GB on Spheron

Inference vs fine-tuning ThinkingCap-Qwen3.6-27B

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 ThinkingCap-Qwen3.6-27B, an on-demand L40S 48GB 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 3.6 step by stepHands-on production setup, GPU configs, and benchmarks for ThinkingCap-Qwen3.6-27B.Read guide

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ThinkingCap-Qwen3.6-27B GPU questions