Best AI Models for NVIDIA GeForce RTX 4080 (16.0GB)
16 GB is a comfortable mid-range tier for local AI. Most 7B–13B models run smoothly at good quantization levels, and smaller models can run at near-full precision.
This memory tier strikes a nice balance between price and capability. Popular 7B models like Llama 3 8B, Mistral 7B, and Qwen 2.5 7B all run very well at Q4_K_M quantization with fast inference and reasonable context windows. You can also fit some larger 13B models at Q3–Q4, though you'll want to keep context lengths modest. Small models like Phi 3 Mini (3.8B) practically fly at Q8 or even FP16 quality.
Runs Well
- 7B models at Q4–Q6 quality with good speed
- Small models (3B–4B) at Q8 or FP16
- 9B models (Gemma 2 9B) at Q4_K_M
Challenging
- 13B–14B models need Q3 or lower
- 30B+ models do not fit in VRAM
- Long context (>8K tokens) with larger models
What LLMs Can NVIDIA GeForce RTX 4080 Run?
Showing compatibility for NVIDIA GeForce RTX 4080
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 1.0 GB6% | 461.3 t/s | 2K | EASY RUN | D28 | |
| Q4_K_M | 0.7 GB4% | 705.9 t/s | 131K | EASY RUN | D27 | |
| Q4_K_M | 0.7 GB4% | 705.9 t/s | 33K | EASY RUN | D27 | |
| Q4_K_M | 1.3 GB8% | 353.0 t/s | 8K | EASY RUN | D29 | |
| Q4_K_M | 15.1 GB95% | 30.8 t/s | 33K | POOR FIT | C33 |
NVIDIA GeForce RTX 4080 Specifications
- Brand
- NVIDIA
- Architecture
- Ada Lovelace
- VRAM
- 16.0 GB GDDR6X
- Memory Bandwidth
- 716.8 GB/s
- CUDA Cores
- 9,728
- Tensor Cores
- 304
- FP16 Performance
- 97.50 TFLOPS
- TDP
- 320W
- Release Date
- 2022-11-16
- MSRP
- $1,199
Get Started
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Intel Arc A770 16GB
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NVIDIA GeForce RTX 4060 Ti 16GB
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Frequently Asked Questions
- Can NVIDIA GeForce RTX 4080 run Llama 3 8B?
Yes, the NVIDIA GeForce RTX 4080 with 16 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 4080 good for AI?
The NVIDIA GeForce RTX 4080 has 16 GB of GDDR6X, making it very good for running local LLM models. Most 7B-13B models run at good quality quantizations.
- How many parameters can NVIDIA GeForce RTX 4080 handle?
With 16 GB, the NVIDIA GeForce RTX 4080 can handle models up to approximately 7-14B parameters depending on quantization. Using Q4_K_M quantization (the typical sweet spot), you can fit roughly 26B parameters.
- What quantization should I use on NVIDIA GeForce RTX 4080?
For the best balance of quality and speed on 16 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 4080 for AI inference?
Speed depends on the model size and quantization. With 716.8 GB/s memory bandwidth, the NVIDIA GeForce RTX 4080 can typically achieve 25-45 tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.