Can the RTX 3090 run dolphin-2.9.1-yi-1.5-34b?
Just. dolphin-2.9.1-yi-1.5-34b at Q4_K_M needs about 21.8 GB of the RTX 3090's 24 GB, running at an estimated 27 tok/s. Keep context short and close other GPU apps.
Every quantization
dolphin-2.9.1-yi-1.5-34b (34.4B) on 24 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 20 GB | 21.8 GB | Tight | ~27 tok/s |
| Q5_K_M | 24 GB | 25.8 GB | Too big | - |
| Q8_0 | 37 GB | 38.8 GB | Too big | - |
| FP16 | 69 GB | 70.8 GB | Too big | - |
Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 3090's 936 GB/s bandwidth (XiongjieDai/GPU-Benchmarks-on-LLM-Inference). Long contexts need more memory and run slower.
Run it
Copy, paste, go
llama-server -hf mradermacher/dolphin-2.9.1-yi-1.5-34b-GGUF -ngl 99Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full dolphin-2.9.1-yi-1.5-34b details →
Compare
Same model, other hardware - and other models on this card
dolphin-2.9.1-yi-1.5-34b on other hardware
- RTX 5090~51 tok/s
- RTX 5090 D~51 tok/s
- RTX 6000D~45 tok/s
- RTX 4090~31 tok/s
- Radeon RX 7900 XTX~28 tok/s
- MacBook Pro 16" M4 Max~16 tok/s
- Arc Pro B60~13 tok/s
- DGX Spark (GB10)~7.8 tok/s
Also runs on the RTX 3090
- GLM-4.7-Flash~211 tok/s
- Qwen3-30B-A3B-Instruct-2507~206 tok/s
- Qwen3.6 27B~34 tok/s
- Qwen3.8 27B~34 tok/s
- Gemma 4 26B A4B~161 tok/s
- gpt-oss-20b~182 tok/s
- Qwen2.5-Coder-14B-Instruct~61 tok/s
- Qwen3 14B~68 tok/s
Frequently asked
Quick answers
Can the NVIDIA RTX 3090 run dolphin-2.9.1-yi-1.5-34b?
Just. dolphin-2.9.1-yi-1.5-34b at Q4_K_M needs about 21.8 GB of the RTX 3090's 24 GB, running at an estimated 27 tok/s. Keep context short and close other GPU apps.
How much VRAM does dolphin-2.9.1-yi-1.5-34b need?
About 20 GB of weights at Q4_K_M, 37 GB at Q8_0 and 69 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.
How fast is dolphin-2.9.1-yi-1.5-34b on the RTX 3090?
Roughly 27 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 3090's 936 GB/s memory bandwidth.