Can the RTX 6000D run OTel-2.0-LLM-31B-IT-QLoRA?

Yes. OTel-2.0-LLM-31B-IT-QLoRA fits the RTX 6000D's 84 GB at Q4_K_M (about 19.8 GB needed), running at an estimated 50 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
84 GB
Needs at Q4
19.8 GB
Headroom
64.2 GB
Speed (Q4_K_M)
~50 tok/s

Every quantization

OTel-2.0-LLM-31B-IT-QLoRA (31.3B) on 84 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M18 GB19.8 GBFits easily~50 tok/s
Q5_K_M22 GB23.8 GBFits easily~41 tok/s
Q8_033 GB34.8 GBFits easily~27 tok/s
FP1663 GB64.8 GBFits~14 tok/s

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 6000D's 1570 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-QLoRA on other hardware

Also runs on the RTX 6000D

Frequently asked

Quick answers

Can the RTX 6000D run OTel-2.0-LLM-31B-IT-QLoRA?

Yes. OTel-2.0-LLM-31B-IT-QLoRA fits the RTX 6000D's 84 GB at Q4_K_M (about 19.8 GB needed), running at an estimated 50 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does OTel-2.0-LLM-31B-IT-QLoRA need?

About 18 GB of weights at Q4_K_M, 33 GB at Q8_0 and 63 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-QLoRA on the RTX 6000D?

Roughly 50 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 6000D's 1570 GB/s memory bandwidth.