Best AI Models for NVIDIA GeForce RTX 4090 (24.0GB)
24 GB is the enthusiast tier for running AI models locally. It comfortably handles 7B–13B models at high quality and opens the door to larger 30B models at moderate quantization.
This is one of the most popular memory tiers for local AI, found in GPUs like the RTX 4090 and RTX 3090. You can run Llama 3 8B, Mistral 7B, and Qwen 2.5 7B at Q5_K_M or Q6_K quality with fast token generation and generous context windows. Larger 14B models like DeepSeek R1 Distill fit comfortably at Q4_K_M. For even bigger models, 30B class runs at Q2–Q3, but 70B models are generally too heavy for single-GPU inference at this tier.
Runs Well
- 7B models (Llama 3 8B, Mistral 7B) at Q5–Q8 quality
- 13B–14B models at Q4–Q5 quality
- Small models (3B–4B) at FP16 precision
- Multimodal models like LLaVA 7B
Challenging
- 30B models only at Q2–Q3 quantization
- 70B models do not fit in VRAM
- Large context windows with 14B+ models
What LLMs Can NVIDIA GeForce RTX 4090 Run?
28 models · 2 excellent · 6 good
Showing compatibility for NVIDIA GeForce RTX 4090
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·36.2 t/s tok/s·131K ctx·GREAT FIT | Q4_K_M | 18.1 GB | 36.2 t/s | 131K | GREAT FIT | S90 |
Q4_K_M·36.5 t/s tok/s·8K ctx·GREAT FIT | Q4_K_M | 18.0 GB | 36.5 t/s | 8K | GREAT FIT | S90 |
Q4_K_M·43.3 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 15.1 GB | 43.3 t/s | 33K | GOOD FIT | A80 |
Q4_K_M·49.3 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 13.3 GB | 49.3 t/s | 131K | GOOD FIT | A70 |
Q4_K_M·33.0 t/s tok/s·41K ctx·GOOD FIT | Q4_K_M | 19.8 GB | 33.0 t/s | 41K | GOOD FIT | A77 |
Q4_K_M·32.0 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 20.5 GB | 32.0 t/s | 131K | GOOD FIT | A70 |
Q4_K_M·32.0 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 20.5 GB | 32.0 t/s | 33K | GOOD FIT | A70 |
Q4_K_M·32.7 t/s tok/s·41K ctx·GOOD FIT | Q4_K_M | 20.0 GB | 32.7 t/s | 41K | GOOD FIT | A73 |
Q4_K_M·71.8 t/s tok/s·16K ctx·FAIR FIT | Q4_K_M | 9.1 GB | 71.8 t/s | 16K | FAIR FIT | B53 |
Q4_K_M·82.7 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 7.9 GB | 82.7 t/s | 33K | FAIR FIT | B48 |
Q4_K_M·118.7 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 5.5 GB | 118.7 t/s | 41K | EASY RUN | C38 |
Q4_K_M·131.3 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 5.0 GB | 131.3 t/s | 33K | EASY RUN | C36 |
Q4_K_M·124.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.3 GB | 124.1 t/s | 131K | EASY RUN | C37 |
Q4_K_M·30.6 t/s tok/s·4K ctx·FAIR FIT | Q4_K_M | 21.4 GB | 30.6 t/s | 4K | FAIR FIT | B56 |
Q4_K_M·107.4 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 6.1 GB | 107.4 t/s | 8K | EASY RUN | C40 |
Q4_K_M·133.2 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 4.9 GB | 133.2 t/s | 33K | EASY RUN | C36 |
NVIDIA GeForce RTX 4090 Specifications
- Brand
- NVIDIA
- Architecture
- Ada Lovelace
- VRAM
- 24.0 GB GDDR6X
- Memory Bandwidth
- 1008.0 GB/s
- CUDA Cores
- 16,384
- Tensor Cores
- 512
- FP16 Performance
- 165.20 TFLOPS
- TDP
- 450W
- Release Date
- 2022-10-12
- MSRP
- $1,599
Get Started
GPUs to Consider Over NVIDIA GeForce RTX 4090
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 5090
NVIDIA · Blackwell
NVIDIA A100 40GB PCIe
NVIDIA · Ampere
NVIDIA RTX 6000 Ada Generation
NVIDIA · Ada Lovelace
NVIDIA V100 SXM2 32GB
NVIDIA · Volta
AMD Radeon PRO W7900
AMD · RDNA 3
NVIDIA L40
NVIDIA · Ada Lovelace
Frequently Asked Questions
- Can NVIDIA GeForce RTX 4090 run Gemma 3 27B IT?
Yes, the NVIDIA GeForce RTX 4090 with 24 GB can run Gemma 3 27B IT, Gemma 2 27B IT, Mistral Small 24B Instruct 2501, and 1130 other models. 125 models run at excellent quality, and 196 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA GeForce RTX 4090 good for AI?
The NVIDIA GeForce RTX 4090 has 24 GB of GDDR6X, making it excellent for running local AI models. It supports 321 models at good quality or better. With 1008.0 GB/s memory bandwidth, it delivers fast token generation speeds. This is an enthusiast-grade GPU that handles most popular open-source LLMs.
- How many parameters can NVIDIA GeForce RTX 4090 handle?
With 24 GB, the NVIDIA GeForce RTX 4090 supports models from 3B to 30B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 40B parameters. This means 7B models at high quality (Q6/Q8) or 30B+ models at Q4.
- What quantization should I use on NVIDIA GeForce RTX 4090?
For the best balance of quality and speed on the NVIDIA GeForce RTX 4090, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. With 24+ GB, you have the headroom to run 7B models at Q5_K_M or even Q6_K for noticeably better output quality. For larger 30B models, Q4_K_M remains the sweet spot.
- How fast is NVIDIA GeForce RTX 4090 for AI inference?
With 1008.0 GB/s memory bandwidth, the NVIDIA GeForce RTX 4090 achieves approximately 146 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~73 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (1008 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on NVIDIA GeForce RTX 4090
~36 tok/s~37 tok/s~43 tok/s~49 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for NVIDIA GeForce RTX 4090?
The top-rated models for the NVIDIA GeForce RTX 4090 are Gemma 3 27B IT, Gemma 2 27B IT, Mistral Small 24B Instruct 2501. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.