Best AI Models for NVIDIA GeForce RTX 3090 (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 3090 Run?
123 models · 67 excellent · 24 good
Showing compatibility for NVIDIA GeForce RTX 3090
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·34.9 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 17.4 GB | 34.9 t/s | 262K | RUNS WELL | A73 |
Q4_K_M·36.7 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 16.6 GB | 36.7 t/s | 262K | RUNS WELL | A73 |
Q4_K_M·33.6 t/s tok/s·131K ctx·RUNS WELL | Q4_K_M | 18.1 GB | 33.6 t/s | 131K | RUNS WELL | A72 |
Q4_K_M·37.7 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 16.1 GB | 37.7 t/s | 262K | RUNS WELL | A74 |
Q4_K_M·32.5 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 18.7 GB | 32.5 t/s | 262K | RUNS WELL | A72 |
Q4_K_M·33.9 t/s tok/s·8K ctx·RUNS WELL | Q4_K_M | 18.0 GB | 33.9 t/s | 8K | RUNS WELL | A73 |
Q4_K_M·32.5 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 18.7 GB | 32.5 t/s | 262K | RUNS WELL | A72 |
Q4_K_M·33.0 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 18.4 GB | 33.0 t/s | 262K | RUNS WELL | A72 |
Q4_K_M·32.5 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 18.7 GB | 32.5 t/s | 262K | RUNS WELL | A72 |
Q4_K_M·40.2 t/s tok/s·131K ctx·RUNS WELL | Q4_K_M | 15.1 GB | 40.2 t/s | 131K | RUNS WELL | A75 |
Q4_K_M·32.6 t/s tok/s·500K ctx·RUNS WELL | Q4_K_M | 18.7 GB | 32.6 t/s | 500K | RUNS WELL | A72 |
Q4_K_M·45.8 t/s tok/s·131K ctx·RUNS WELL | Q4_K_M | 13.3 GB | 45.8 t/s | 131K | RUNS WELL | A78 |
Q4_K_M·36.1 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 16.9 GB | 36.1 t/s | 262K | RUNS WELL | A73 |
BF16·39.5 t/s tok/s·4K ctx·RUNS WELL | BF16 | 15.4 GB | 39.5 t/s | 4K | RUNS WELL | A75 |
Q4_K_M·41.0 t/s tok/s·33K ctx·RUNS WELL | Q4_K_M | 14.9 GB | 41.0 t/s | 33K | RUNS WELL | A75 |
BF16·39.6 t/s tok/s·8K ctx·RUNS WELL | BF16 | 15.3 GB | 39.6 t/s | 8K | RUNS WELL | A75 |
NVIDIA GeForce RTX 3090 Specifications
- Brand
- NVIDIA
- Architecture
- Ampere
- Compute Capability
- 8.6 (CUDA SM version)
- VRAM
- 24.0 GB GDDR6X
- Memory Bandwidth
- 936.2 GB/s
- CUDA Cores
- 10,496
- Tensor Cores
- 328
- FP16 Performance
- 71.00 TFLOPS
- TDP
- 350W
- Release Date
- 2020-09-24
- MSRP
- $1,499
Get Started
Prompt Processing
Estimated for Qwen Marketing, the compute-bound phase that reads your prompt before the first reply token appears.
Short chat
262 ms
512 tok prompt
Long chat
2.1 s
4,096 tok prompt
Document / codebase
16.8 s
32,768 tok prompt
Prefill is compute-bound and a different number from the decode tok/s shown elsewhere on this page — how prompt processing works →
Efficiency & Value
Based on Qwen Marketing at ~33.8 tok/s decode.
Tokens per watt
Higher is better.
$ per tok/s (MSRP)
MSRP-based, not street price. Lower is better.
Performance figures are estimates calibrated as of 2026-07-30 — see calibration basis →
GPUs to Consider Over NVIDIA GeForce RTX 3090
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 5090
NVIDIA · Blackwell
NVIDIA GeForce RTX 3090 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 4090
NVIDIA · Ada Lovelace
AMD Radeon RX 7900 XTX
AMD · RDNA 3
Frequently Asked Questions
- Can NVIDIA GeForce RTX 3090 run Qwen3.6 27B?
Yes, the NVIDIA GeForce RTX 3090 with 24 GB can run Qwen3.6 27B, Gemma 4 26B A4B IT, Gemma 3 27B IT, and 1520 other models. 1076 models run at excellent quality, and 305 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA GeForce RTX 3090 good for AI?
The NVIDIA GeForce RTX 3090 has 24 GB of GDDR6X, making it excellent for running local AI models. It supports 1381 models at good quality or better. With 936.2 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 3090 handle?
With 24 GB, the NVIDIA GeForce RTX 3090 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 3090?
For the best balance of quality and speed on the NVIDIA GeForce RTX 3090, 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 3090 for AI inference?
With 936.2 GB/s memory bandwidth, the NVIDIA GeForce RTX 3090 achieves approximately 135 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~68 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (936.2 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 3090
~35 tok/s~37 tok/s~34 tok/s~38 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 3090?
The top-rated models for the NVIDIA GeForce RTX 3090 are Qwen3.6 27B, Gemma 4 26B A4B IT, Gemma 3 27B IT. 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.
- What power supply and cooling does NVIDIA GeForce RTX 3090 need?
The NVIDIA GeForce RTX 3090 has a TDP of 350 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 750 W PSU or larger. At this power level, a high-airflow case matters: aim for at least two front intake fans and one rear exhaust, with tidy cabling so hot air isn't trapped around the card. LLM inference sustains full GPU load continuously — longer and more consistently than most gaming workloads — so also make sure your CPU cooler can keep up under combined load.