Can the RTX 6000D run Qwen2.5 72B?

Yes. Qwen2.5 72B fits the RTX 6000D's 84 GB at Q4_K_M (about 41.8 GB needed), running at an estimated 23 tok/s. There's room for up to Q8_0 if you want higher quality.

VRAM
84 GB
Needs at Q4
41.8 GB
Headroom
42.2 GB
Speed (Q4_K_M)
~23 tok/s

Every quantization

Qwen2.5 72B (72B) on 84 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M40 GB41.8 GBFits easily~23 tok/s
Q5_K_M48 GB49.8 GBFits easily~19 tok/s
Q8_078 GB79.8 GBTight~12 tok/s
FP16144 GB145.8 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 6000D's 1570 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

Run it

Copy, paste, go

ollama run qwen2.5:72b
llama-server -hf Qwen/Qwen2.5-72B-Instruct-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen2.5 72B details →

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Same model, other hardware - and other models on this card

Qwen2.5 72B on other hardware

Also runs on the RTX 6000D

Frequently asked

Quick answers

Can the RTX 6000D run Qwen2.5 72B?

Yes. Qwen2.5 72B fits the RTX 6000D's 84 GB at Q4_K_M (about 41.8 GB needed), running at an estimated 23 tok/s. There's room for up to Q8_0 if you want higher quality.

How much VRAM does Qwen2.5 72B need?

About 40 GB of weights at Q4_K_M, 78 GB at Q8_0 and 144 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen2.5 72B on the RTX 6000D?

Roughly 23 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.