Best AI Models for NVIDIA GeForce GTX 1080 Ti (11.0GB)
11 GB is an entry-level tier for local AI. You can run small 7B models at lower quantization levels, which is great for experimenting but comes with quality and speed trade-offs.
With 11 GB, you're limited to smaller models and lower quantization levels, but it's still enough for a meaningful local AI experience. Phi 3 Mini (3.8B) and similar compact models run well at Q4_K_M. For 7B models like Mistral 7B and Llama 3 8B, you'll need Q2_K or Q3_K_M quantization, which reduces output quality. Think of this tier as ideal for learning and experimentation rather than production workloads.
Runs Well
- 3B–4B models at Q4–Q5 quality
- 7B models at Q2–Q3 (usable but reduced quality)
- Quick experiments and learning
Challenging
- 7B models at Q4+ (VRAM too tight)
- Any model above 7B parameters
- Long context windows even with small models
What LLMs Can NVIDIA GeForce GTX 1080 Ti Run?
Showing compatibility for NVIDIA GeForce GTX 1080 Ti
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 7.9 GB72% | 39.8 t/s | 33K | GREAT FIT | S88 | |
| Q4_K_M | 9.1 GB83% | 34.5 t/s | 16K | GOOD FIT | A77 | |
| Q4_K_M | 5.5 GB50% | 57.0 t/s | 41K | GOOD FIT | A65 | |
| Q4_K_M | 6.1 GB55% | 51.6 t/s | 8K | GOOD FIT | A70 | |
| Q4_K_M | 5.3 GB48% | 59.6 t/s | 131K | FAIR FIT | B63 | |
| Q4_K_M | 5.0 GB45% | 63.1 t/s | 33K | FAIR FIT | B60 | |
| Q4_K_M | 5.4 GB49% | 58.6 t/s | 131K | FAIR FIT | B64 | |
| Q4_K_M | 5.4 GB49% | 58.4 t/s | 131K | FAIR FIT | B64 |
NVIDIA GeForce GTX 1080 Ti Specifications
- Brand
- NVIDIA
- Architecture
- Pascal
- VRAM
- 11.0 GB GDDR5X
- Memory Bandwidth
- 484.4 GB/s
- CUDA Cores
- 3,584
- Tensor Cores
- 0
- FP16 Performance
- 11.30 TFLOPS
- TDP
- 250W
- Release Date
- 2017-03-10
- MSRP
- $699
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Frequently Asked Questions
- Can NVIDIA GeForce GTX 1080 Ti run Llama 3 8B?
Yes, the NVIDIA GeForce GTX 1080 Ti with 11 GB can run Llama 3 8B at Q4_K_M quantization with good performance. At this VRAM level, you can expect smooth token generation and responsive inference for chat and coding tasks.
- Is NVIDIA GeForce GTX 1080 Ti good for AI?
The NVIDIA GeForce GTX 1080 Ti has 11 GB of GDDR5X, making it usable for running local LLM models. Small models run well, but larger 7B models need lower quantization.
- How many parameters can NVIDIA GeForce GTX 1080 Ti handle?
With 11 GB, the NVIDIA GeForce GTX 1080 Ti can handle models up to approximately 3-7B parameters depending on quantization. Using Q4_K_M quantization (the typical sweet spot), you can fit roughly 18B parameters.
- What quantization should I use on NVIDIA GeForce GTX 1080 Ti?
For the best balance of quality and speed on 11 GB, Q4_K_M is the recommended starting point. If you have headroom, try Q5_K_M for better quality. For larger models that barely fit, Q3_K_M or Q2_K can squeeze them in at the cost of some output quality.
- How fast is NVIDIA GeForce GTX 1080 Ti for AI inference?
Speed depends on the model size and quantization. With 484.4 GB/s memory bandwidth, the NVIDIA GeForce GTX 1080 Ti can typically achieve 15-35 tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.