NVIDIA GeForce RTX 3090 for local LLMs
Written by Jakub Rusinowski · Last updated
With 24 GB at 936 GB/s, the RTX 3090 runs 111 catalogued models at Q4_K_M with 8K context. The largest that fits is Qwen 3 32B (~22.8 GB), and the top pick is Laguna XS 2.1 33B-A3B at 80–154 tok/s.
Still excellent for AI. 24 GB VRAM on par with RTX 4090 for model compatibility. Available used for $400–600. Slightly slower but fantastic value.
Models that run on the RTX 3090
Q4_K_M, 8K context, 24 GB usable. Ranked by quality and speed.
| Model | Memory · marker = 24 GB | VRAM | Speed |
|---|---|---|---|
| Laguna XS 2.1 33B-A3B Poolside Laguna XS 2.1 | 20.7 GB | 80–154 tok/s | |
| Granite 4.0 Small-H 32B-A9B IBM Granite 4.0 | 20.1 GB | 46–89 tok/s | |
| Olmo 3 32B Olmo 3 | 20.1 GB | 18–34 tok/s | |
| Nemotron-Cascade 2 30B-A3B Nemotron Cascade 2 | 19.9 GB | 76–146 tok/s | |
| Gemma 4 31B Gemma 4 | 19.5 GB | 18–35 tok/s | |
| Qwen 3 30B-A3B (MoE) Qwen 3 | 20 GB | 97–187 tok/s | |
| Qwen3-Coder 30B-A3B (MoE) Qwen3-Coder | 20 GB | 97–187 tok/s | |
| GLM-4.7-Flash 30B-A3B GLM-4.7 / GLM-Z1 | 18.9 GB | 81–156 tok/s | |
| Granite 4.1 30B IBM Granite 4.1 | 18.9 GB | 19–36 tok/s | |
| Nemotron 3 Nano Omni 30B-A3B Nemotron 3 Nano Omni | 18.9 GB | 81–156 tok/s | |
| North Mini Code 1.0 30B-A3B North Mini Code | 18.9 GB | 81–156 tok/s | |
| Granite 4.2 30B IBM Granite 4.2 | 18.9 GB | 19–36 tok/s |
Buy it or rent the same memory
Buy the card, or rent a GPU with the same memory by the hour to try models first.
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Speed vs other GPUs
Llama 3.1 8B, Q4_K_M. Estimated ranges. How this is calculated
Specifications
Specs last updated 2026-07-12.
- Memory
- 24 GB
- Memory bandwidth
- 936 GB/s
- Architecture
- Ampere GA102
- Series
- RTX 30-series
- Board power
- 350 W
- Release year
- 2020
- Launch price
- $1,499
- Usable for models
- 24 GB
Similar GPUs
Frequently asked questions
Can the NVIDIA GeForce RTX 3090 run local LLMs?
Yes. With 24 GB (24 GB usable by a model) it runs 111 of the catalogued models at Q4_K_M with 8K context; the largest is Qwen 3 32B, needing about 22.8 GB.
How fast is the NVIDIA GeForce RTX 3090 for AI inference?
It is estimated to run Llama 3.1 8B at 60–115 tok/s at Q4_K_M. Llama 3.3 70B does not fit: it needs about 44 GB against 24 GB usable. These are modelled estimates from memory bandwidth, not measurements; the methodology page shows the formula.
What LLMs can I run on 24 GB?
Among the best that fit: Laguna XS 2.1 33B-A3B, Granite 4.0 Small-H 32B-A9B, Olmo 3 32B, Nemotron-Cascade 2 30B-A3B, Gemma 4 31B. The quickest start is Ollama: ollama run deepseek-r1:32b.
Keep going
Can I run it on the RTX 3090?
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