Can the RTX 4090 run OTel-2.0-LLM-31B-IT?
Just. OTel-2.0-LLM-31B-IT at Q4_K_M needs about 20.8 GB of the RTX 4090's 24 GB, running at an estimated 33 tok/s. Keep context short and close other GPU apps.
Every quantization
OTel-2.0-LLM-31B-IT (32.1B) on 24 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 19 GB | 20.8 GB | Tight | ~33 tok/s |
| Q5_K_M | 23 GB | 24.8 GB | Too big | - |
| Q8_0 | 34 GB | 35.8 GB | Too big | - |
| FP16 | 64 GB | 65.8 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 4090's 1008 GB/s bandwidth (XiongjieDai/GPU-Benchmarks-on-LLM-Inference). Long contexts need more memory and run slower.
Compare
Same model, other hardware - and other models on this card
OTel-2.0-LLM-31B-IT on other hardware
- RTX 5090~54 tok/s
- RTX 5090 D~54 tok/s
- RTX 6000D~47 tok/s
- RTX 3090~29 tok/s
- Radeon RX 7900 XTX~29 tok/s
- MacBook Pro 16" M4 Max~16 tok/s
- Arc Pro B60~14 tok/s
- DGX Spark (GB10)~8.3 tok/s
Also runs on the RTX 4090
- OTel-2.0-LLM-31B-IT-QLoRA~35 tok/s
- Qwen3.6 27B~39 tok/s
- Qwen3.8 27B~39 tok/s
- Qwen3.8-27B-OBLITERATED~39 tok/s
- Gemma 4 26B A4B~184 tok/s
- gpt-oss-20b~208 tok/s
- Muse Glimmer~63 tok/s
- Nemotron 3.5 Lightning~63 tok/s
Frequently asked
Quick answers
Can the NVIDIA RTX 4090 run OTel-2.0-LLM-31B-IT?
Just. OTel-2.0-LLM-31B-IT at Q4_K_M needs about 20.8 GB of the RTX 4090's 24 GB, running at an estimated 33 tok/s. Keep context short and close other GPU apps.
How much VRAM does OTel-2.0-LLM-31B-IT need?
About 19 GB of weights at Q4_K_M, 34 GB at Q8_0 and 64 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.
How fast is OTel-2.0-LLM-31B-IT on the RTX 4090?
Roughly 33 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 4090's 1008 GB/s memory bandwidth.