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.
| VRAM | 24 GB |
| Memory Bandwidth | 936 GB/s |
| TDP | 350 W |
| Architecture | Ampere GA102 |
| Release Year | 2020 |
| MSRP at Launch | $1,499 |
| Inference Speed (Llama 3.1 8B Q4_K_M) | ~115 tokens/sec |
| Inference Speed (Llama 3.3 70B Q4_K_M) | ~22 tokens/sec |
All models below run comfortably in 24 GB VRAM with Q4_K_M quantization.
| Llama 3.3 | 43 GB VRAM (smallest variant — needs more VRAM or a lower quant) · Q2_K_XS (Tight) · ollama run llama3.3 |
| Llama 3.1 Family | 6 GB VRAM · Q4_K_M · ollama run llama3.1 |
| DeepSeek R1 | 20 GB VRAM · Q4_K_M · ollama run deepseek-r1:32b |
| Qwen 3 | 20 GB VRAM · Q4_K_M · ollama run qwen3:32b |
| Gemma 3 | 17 GB VRAM · Q4_K_M · ollama run gemma3:27b |
| Mistral Small 3.1 | 14 GB VRAM · Q4_K_M · ollama run mistral-small3.1 |
| Phi-4 Family | 9 GB VRAM · Q4_K_M · ollama run phi4 |
Install Ollama then run the recommended model for this GPU:
ollama run deepseek-r1:32b
Yes — the NVIDIA GeForce RTX 3090 has 24 GB VRAM and runs Still excellent for AI. 24 GB VRAM on par with RTX 4090 for model compatibility. Available used for $400–600. Slightly s
The NVIDIA GeForce RTX 3090 runs Llama 3.1 8B at ~115 tokens/sec with Q4_K_M quantization. For the 70B model it achieves ~22 tokens/sec.
With 24 GB you can run: Llama 3.3, Llama 3.1 Family, DeepSeek R1, Qwen 3, Gemma 3. Use Ollama for the easiest setup: ollama run deepseek-r1:32b.
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