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NVIDIA RTX 2080 Ti Server
NVIDIA RTX 2080 Ti Server is a budget training-capable GPU cloud server available from Immers Cloud. The RTX 2080 Ti was NVIDIA's flagship consumer Turing GPU and remains a viable option for budget ML training and inference.
Specifications
| Component | Specification |
|---|---|
| GPU | NVIDIA GeForce RTX 2080 Ti (Turing architecture) |
| VRAM | 11 GB GDDR6 |
| CUDA Cores | 4,352 |
| Memory Bandwidth | 616 GB/s |
| Tensor Cores | 2nd Generation (FP16) |
| TDP | 250W |
| Starting Price | From $0.28/hr |
Performance
The RTX 2080 Ti was the first widely-adopted consumer GPU for ML training, and it still holds up for smaller workloads:
- 4,352 CUDA cores — more raw cores than Tesla T4 or A2
- 11 GB GDDR6 — fits small-to-medium models
- 616 GB/s bandwidth — nearly double the Tesla T4
- 2nd-gen Tensor Cores — FP16 mixed-precision training support
Compared to inference-only GPUs (T4, A2), the 2080 Ti can actually train models thanks to its higher CUDA core count and memory bandwidth. It's roughly:
- 2–3x faster than Tesla T4 for training
- 50–60% slower than RTX 3090 for training
- Competitive with the RTX 3080 for workloads fitting in 11 GB
The 11 GB VRAM limits model size to approximately 3B parameters in FP16 or 7B with aggressive quantization.
Best Use Cases
- Budget ML training for small models
- Fine-tuning smaller language models (up to 3B FP16)
- Computer vision model training
- AI image generation (Stable Diffusion at standard resolution)
- Learning and education in ML/deep learning
- Inference of medium models
- Kaggle competitions on a budget
Pros and Cons
Advantages
- $0.28/hr — cheapest option capable of real training
- 4,352 CUDA cores — good compute for the price
- 616 GB/s bandwidth — sufficient for training
- Tensor Cores enable mixed-precision training
- Proven GPU with years of community knowledge
Limitations
- Only 11 GB VRAM — limits model size significantly
- Older Turing architecture (no FP8, BF16, TF32)
- No ECC memory
- No NVLink for multi-GPU
- Previous generation — limited future optimization
Pricing
Available from Immers Cloud starting at $0.28/hr. Monthly cost for 24/7: approximately $202. The cheapest GPU that can realistically train models.
Recommendation
The NVIDIA RTX 2080 Ti Server is the budget training GPU. If you're learning ML, running experiments, or training small models and every dollar counts, this is your entry point. The 11 GB VRAM is the main constraint — if you need more, jump to the NVIDIA RTX 3090 Server ($0.75/hr, 24 GB). For pure inference, the NVIDIA Tesla T4 Server ($0.23/hr) may be more cost-effective.