Best Local LLM for the RTX 3090
The RTX 3090's 24 GB of VRAM runs Ornith 1.0 35B, the top open-weight coding model on SWE-bench Verified, plus Gemma 3 27B and everything lighter at Q4. VRAM capacity matters more than raw compute for local inference, which is why this card remains a popular route to 24 GB.
Model data last verified on June 27, 2026. All VRAM figures are for the Q4 quantization Ollama serves by default.
The hardware in question
The RTX 3090 is two generations old and out of production, so it is not in our tracked hardware database. If you are buying new, the 24 GB cards we track are the AMD RX 7900 XTX 24GB and the NVIDIA RTX 4090 24GB.
Every model that fits 24 GB of VRAM
13 of the 16 models in our verified catalog fit this budget at Q4, sorted with the largest fit first. Click any model for its full spec sheet, hardware cross-reference and run commands.
| Model | Params | Quant | Min VRAM | Context | Fit |
|---|---|---|---|---|---|
| Ornith 1.0 35B#1 SWE-bench open | 35B | Q4 | 20 GB | 262K | 4 GB free |
| Gemma 3 27B | 27B | Q4 | 15 GB | 128K | 9 GB free |
| OpenAI gpt-oss-20bHistoric first | 20B total / 3.6B active (MoE) | Q4 | 12 GB | 128K | 12 GB free |
| Gemma 4 12BBest for fine-tuning | 12B | Q4 | 8 GB | 256K | 16 GB free |
| Phi-4 14BBest small coder | 14B | Q4 | 8 GB | 16K | 16 GB free |
| Mistral Nemo 12B | 12B | Q4 | 7 GB | 128K | 17 GB free |
| Llama 3.1 8B | 8B | Q4 | 5 GB | 128K | 19 GB free |
| Qwen3 8BMost popular | 8B | Q4 | 5 GB | 128K | 19 GB free |
| DeepSeek R1 Distill 8BMost private | 8B | Q4 | 5 GB | 128K | 19 GB free |
| Mistral 7B v0.3 | 7B | Q4 | 4 GB | 32K | 20 GB free |
| Qwen 2.5 Coder 7B | 7B | Q4 | 4 GB | 128K | 20 GB free |
| Gemma 3 4B | 4B | Q4 | 3 GB | 128K | 21 GB free |
| Llama 3.2 3BFastest | 3B | Q4 | 2 GB | 128K | 22 GB free |
Run the top picks with Ollama
One command each. Ollama pulls the Q4_K_M build by default and exposes an OpenAI-compatible endpoint at localhost:11434/v1.
ollama run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUFollama run gemma3:27bollama run gpt-oss:20bFrequently asked questions
Is the RTX 3090 still good for local AI in 2026?
Yes. For local inference, VRAM capacity matters more than raw compute, and the 3090's 24 GB matches the newer RTX 4090. It fits the same set of models; token generation is slower than on current-generation cards, but every model on this page still runs.
What is the biggest model an RTX 3090 can run?
Ornith 1.0 35B at Q4, which needs about 20 GB, is the largest model in our catalog that fits in 24 GB. Gemma 3 27B at about 15 GB leaves more headroom for long context.
Can an RTX 3090 run Llama 3.3 70B?
Not on one card. Llama 3.3 70B needs about 48 GB at Q4_K_M, which means a dual-GPU rig or a Mac with 48 GB of unified memory.
RTX 3090 or RTX 4090 for local LLMs?
They fit the same models, since both have 24 GB. The 4090 generates faster but is out of production and sells for around $2,755 in scarce stock. If your priority is capacity per dollar, a used 3090 covers the identical model list.
Different budget or use case?
The faceted model browser combines VRAM, family, license and task filters over the same verified catalog.