Best AI Models for NVIDIA GeForce RTX 3080 (10.0GB)
10 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 10 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 RTX 3080 Run?
Showing compatibility for NVIDIA GeForce RTX 3080
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 5.0 GB50% | 99.0 t/s | 131K | GOOD FIT | A65 | |
| Q8_0 | 4.9 GB49% | 100.7 t/s | 4K | FAIR FIT | B64 | |
| Q4_K_M | 9.1 GB91% | 54.2 t/s | 16K | FAIR FIT | B48 | |
| Q4_K_M | 2.9 GB29% | 171.0 t/s | 41K | EASY RUN | C44 | |
| Q4_K_M | 2.9 GB29% | 173.4 t/s | 131K | EASY RUN | C44 | |
| Q4_K_M | 2.6 GB26% | 187.2 t/s | 2K | EASY RUN | C41 | |
| Q4_K_M | 2.0 GB20% | 249.6 t/s | 131K | EASY RUN | C35 | |
| Q4_K_M | 1.0 GB10% | 489.3 t/s | 2K | EASY RUN | C30 |
NVIDIA GeForce RTX 3080 Specifications
- Brand
- NVIDIA
- Architecture
- Ampere
- VRAM
- 10.0 GB GDDR6X
- Memory Bandwidth
- 760.3 GB/s
- CUDA Cores
- 8,704
- Tensor Cores
- 272
- FP16 Performance
- 59.50 TFLOPS
- TDP
- 320W
- Release Date
- 2020-09-17
- MSRP
- $699
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Frequently Asked Questions
- Can NVIDIA GeForce RTX 3080 run Llama 3 8B?
Yes, the NVIDIA GeForce RTX 3080 with 10 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 RTX 3080 good for AI?
The NVIDIA GeForce RTX 3080 has 10 GB of GDDR6X, 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 RTX 3080 handle?
With 10 GB, the NVIDIA GeForce RTX 3080 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 16B parameters.
- What quantization should I use on NVIDIA GeForce RTX 3080?
For the best balance of quality and speed on 10 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 RTX 3080 for AI inference?
Speed depends on the model size and quantization. With 760.3 GB/s memory bandwidth, the NVIDIA GeForce RTX 3080 can typically achieve 15-35 tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.