NVIDIA RTX 3090 Ti
The NVIDIA RTX 3090 Ti has 24 GB VRAM and 1008 GB/s memory bandwidth. It can run 42 of our 70 tracked models natively in VRAM at 8k context.
With 24 GB GDDR6X, the NVIDIA RTX 3090 Ti is a consumer-tier GPU that can run 42 models natively. It handles 70B-class models at Q4 quantization.
NVIDIA RTX 3090 Ti: March 2022 refresh with 24GB at 1008 GB/s — slightly faster than 3090.
Same model compatibility as 3090 with ~10% higher tokens/sec.
Full CUDA support. Rare and expensive — regular 3090 is better value.
| Vendor | NVIDIA |
| Architecture | Ampere |
| VRAM | 24 GB |
| Memory type | GDDR6X |
| Memory bandwidth | 1008 GB/s |
| Compute backend | CUDA |
| Tier | Consumer |
| Released | 2022 |
| Models (native) | 42 / 70 |
| Models (offload) | 11 / 70 |
Software: Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.
Popular models for this GPU
Models this GPU runs natively in VRAM (42)
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q2_K · ~261.3 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 84.2NVFP4 · ~739.2 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2NVFP4 · ~57.6 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0NVFP4 · ~58.6 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5NVFP4 · ~61.5 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0NVFP4 · ~62 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4NVFP4 · ~62 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0NVFP4 · ~62 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3NVFP4 · ~739.2 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2NVFP4 · ~65 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5NVFP4 · ~739.2 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0NVFP4 · ~74.1 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5NVFP4 · ~74.7 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2NVFP4 · ~74.7 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6NVFP4 · ~583.6 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8NVFP4 · ~84 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2NVFP4 · ~90.8 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9NVFP4 · ~554.4 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0NVFP4 · ~136.2 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7NVFP4 · ~137.1 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4NVFP4 · ~144 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6NVFP4 · ~165.2 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6NVFP4 · ~165.2 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0NVFP4 · ~219.1 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~63 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~63 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~63 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~66.3 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~69.5 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~63 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~63 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~66.3 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~78.8 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~81.3 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~96.9 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~126 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~148.2 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~168 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~203.2 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~252 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~504 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~700 t/s
Models that fit with CPU offload (11)
These use system RAM for layers that don't fit in VRAM — expect much slower inference.
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q2_K · ~76.6 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q2_K · ~63.8 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7Q2_K · ~153.2 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q2_K · ~45.1 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q2_K · ~63.8 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q2_K · ~63.8 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1NVFP4 · ~7 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9NVFP4 · ~7.2 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0NVFP4 · ~7.2 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4NVFP4 · ~7.2 t/s
- Command-R 35B35B · MMLU-Pro 33.0NVFP4 · ~14.4 t/s
Too large for this GPU (17)
Compare NVIDIA RTX 3090 Ti with other GPUs
Frequently asked questions
- How much VRAM does the NVIDIA RTX 3090 Ti have?
- The NVIDIA RTX 3090 Ti has 24 GB of GDDR6X with 1008 GB/s memory bandwidth.
- What is the NVIDIA RTX 3090 Ti best for?
- With 24 GB of VRAM, the NVIDIA RTX 3090 Ti is well-suited for running 7B–32B models at Q4 with room for context, making it a great all-rounder for local LLM inference.
- What LLMs can the NVIDIA RTX 3090 Ti run locally?
- The NVIDIA RTX 3090 Ti can run 42 of the 70 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at BF16, Llama 3.2 3B Instruct at FP32, Llama 3.2 1B Instruct at FP32.
- Can the NVIDIA RTX 3090 Ti run Llama 3.3 70B Instruct?
- The NVIDIA RTX 3090 Ti can run Llama 3.3 70B Instruct with CPU offload at NVFP4 quantization, but inference will be slower than native VRAM execution.
- Can the NVIDIA RTX 3090 Ti run Qwen 3.6 27B?
- Yes. The NVIDIA RTX 3090 Ti runs Qwen 3.6 27B natively in VRAM at NVFP4 quantization, achieving approximately 74.7 tokens per second.
- Can the NVIDIA RTX 3090 Ti run Llama 3.1 8B Instruct?
- Yes. The NVIDIA RTX 3090 Ti runs Llama 3.1 8B Instruct natively in VRAM at BF16 quantization, achieving approximately 63 tokens per second.