What runs this LLM?
Pick the model you want to serve. We size it from the released checkpoint and list the cheapest configurations you can rent today that hold it — with the on-demand and serverless rate for each.
1 · Choose a model
Context-length sizing isn’t published for this architecture.
2 · What it needs
Qwen3.6 27B at 16-bit
67 GB of VRAM
at short context — measured from the released checkpoint, not estimated from parameter count.
- Model weights
- 56 GB
- KV cachefolded into overhead
- —
- Runtime overhead20% of weights
- 11 GB
About this model
- Parameters
- 27.8B
- Released at
- 16-bit
- Measured weights
- 56 GB
3 · Where to run it
Bookable configurations
Cheapest first, checked against 98 GPU types with live bookable capacity.
| Configuration | Total VRAM | On-demand | Serverless | GPU details |
|---|---|---|---|---|
| 8× NVIDIA Tesla V100 16GB | 128 GB 61 GB free | $0.24/hr $0.03/GPU-hr  Vast.ai | — | See all prices |
| 6× NVIDIA RTX 3060 | 72 GB 5.3 GB free | $0.30/hr $0.05/GPU-hr Vast.ai | — | See all prices |
| 7× NVIDIA GTX 1070Tight fit | 56 GB 11 GB short of the margin | $0.35/hr $0.05/GPU-hr Vast.ai | — | See all prices |
| 4× NVIDIA Tesla P100Tight fit | 64 GB 2.7 GB short of the margin | $0.36/hr $0.09/GPU-hr Vast.ai | — | See all prices |
| 7× NVIDIA Titan Xp | 84 GB 17 GB free | $0.42/hr $0.06/GPU-hr Vast.ai | — | See all prices |
| 7× NVIDIA GTX 1080 Ti | 77 GB 10 GB free | $0.42/hr $0.06/GPU-hr Vast.ai | — | See all prices |
| 6× NVIDIA RTX A4000 | 96 GB 29 GB free | $0.42/hr $0.07/GPU-hr Vast.ai | — | See all prices |
| 1× NVIDIA A100 80GB | 80 GB 13 GB free | $0.43/hr Vast.ai | $1.97/hr  Verda | See all prices |
Showing the 8 cheapest. Model weights plus 20% for runtime overhead, from measured checkpoint sizes; a stated context length adds its KV cache on top. Each price is the cheapest bookable rate for that exact instance size, for the whole configuration — the cheaper of the two is highlighted and orders the table. A ×N figure is total VRAM across N cards and makes no claim about interconnect throughput.
How this is worked out
Weight sizes are measured from the actual checkpoint files on Hugging Face — never derived from parameter count, which gets quantized releases wrong. On top of the weights we assume 20% for activations, CUDA context and allocator slack. Stating a context length adds its KV cache explicitly, computed only for architectures whose cache shape the config states unambiguously.
A configuration that holds the weights and cache but not that full margin is marked as a tight fit rather than hidden — it will load, and may well run at batch size one, but expect trouble under load.
Prices are the cheapest bookable rate for that exact instance size, refreshed with the rest of the catalog.