NVIDIA GTX 1080 Ti GPU: Specs, VRAM, Price & Benchmarks (2026)
The GTX 1080 Ti is a real GPU (launched 2017), but no provider on Aquanode is listing it for rental right now, so there is no live hourly price to quote. Availability changes as providers add and retire hardware; the specs, VRAM and price context below still apply.
How much VRAM does the GTX 1080 Ti have?
The GTX 1080 Ti has 11GB GDDR5X, with 484 GB/s of peak memory bandwidth.
GTX 1080 Ti VRAM calculator: check which models fit in its memory at each precision.
All models that fit in 8 GB: the open models whose weights and overhead fit, at native, FP8 and INT4 precision.
What can the GTX 1080 Ti run?
Popular open models from small to frontier scale, with the memory each needs and how many GTX 1080 Ti cards (11GB GDDR5X each) that takes.
| Model | As published | FP8 | INT4 |
|---|---|---|---|
| Qwen/Qwen3-8B 8.2B | BF16: not supported | FP8: not supported | INT4: not supported |
| Qwen/Qwen2.5-14B-Instruct 14.8B | BF16: not supported | FP8: not supported | INT4: not supported |
| Qwen/Qwen3-32B 32.8B | BF16: not supported | FP8: not supported | INT4: not supported |
| Qwen/Qwen-72B 72.3B | BF16: not supported | FP8: not supported | INT4: not supported |
| MiniMaxAI/MiniMax-M2.7 228.7B | FP8: not supported | – | INT4: not supported |
| deepseek-ai/DeepSeek-R1 684.5B | FP8: not supported | – | INT4: not supported |
Estimates: weights at the stated precision plus a flat 20% for KV cache and overhead, at a moderate context length. A dash means the precision is not offered for that model (it is already published at that size). INT4 needs a published quantized checkpoint. Open any model for a per-GPU breakdown, or use the GTX 1080 Ti VRAM calculator.
GTX 1080 Ti specs
| Architecture | launched 2017 |
| VRAM | 11GB GDDR5X |
| Memory bandwidth | 484 GB/s |
| TDP | 250W |
| Form factor | PCIe |
Specs sourced from the vendor's public product page. See the source.
GPU Glossary: What is VRAM?, Tensor Cores, CUDA Cores, TFLOPS
Good for
11GB is enough for a 7B model at INT8 or a 13B at INT4, useful for quantized inference experiments, embeddings and classic CV.
Not good for
Pascal has no tensor cores and no BF16 or FP8, and INT4 serving kernels (AWQ, GPTQ) need compute capability 7.5, so it falls back to llama.cpp-style GGUF runs. Slow for anything compute-bound.
Related guides
Other models in the same generation. The full list is in the GPU index.
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