Can the RTX 5090 run OTel-2.0-LLM-31B-IT?

Yes. OTel-2.0-LLM-31B-IT fits the RTX 5090's 32 GB at Q4_K_M (about 20.8 GB needed), running at an estimated 54 tok/s. There's room for up to Q5_K_M if you want higher quality.

VRAM
32 GB
Needs at Q4
20.8 GB
Headroom
11.2 GB
Speed (Q4_K_M)
~54 tok/s

Every quantization

OTel-2.0-LLM-31B-IT (32.1B) on 32 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M19 GB20.8 GBFits~54 tok/s
Q5_K_M23 GB24.8 GBFits~45 tok/s
Q8_034 GB35.8 GBToo big -
FP1664 GB65.8 GBToo big -

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 5090's 1792 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). 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

Also runs on the RTX 5090

Frequently asked

Quick answers

Can the NVIDIA RTX 5090 run OTel-2.0-LLM-31B-IT?

Yes. OTel-2.0-LLM-31B-IT fits the RTX 5090's 32 GB at Q4_K_M (about 20.8 GB needed), running at an estimated 54 tok/s. There's room for up to Q5_K_M if you want higher quality.

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 5090?

Roughly 54 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5090's 1792 GB/s memory bandwidth.