Can the RTX 6000D run Qwen3 14B?

Yes. Qwen3 14B fits the RTX 6000D's 84 GB at Q4_K_M (about 9.8 GB needed), running at an estimated 113 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
84 GB
Needs at Q4
9.8 GB
Headroom
74.2 GB
Speed (Q4_K_M)
~113 tok/s

Every quantization

Qwen3 14B (14B) on 84 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M8 GB9.8 GBFits easily~113 tok/s
Q5_K_M10 GB11.8 GBFits easily~90 tok/s
Q8_016 GB17.8 GBFits easily~56 tok/s
FP1628 GB29.8 GBFits easily~32 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.

Run it

Copy, paste, go

ollama run qwen3:14b
llama-server -hf Qwen/Qwen3-14B-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3 14B details →

Compare

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

Qwen3 14B on other hardware

Also runs on the RTX 6000D

Frequently asked

Quick answers

Can the RTX 6000D run Qwen3 14B?

Yes. Qwen3 14B fits the RTX 6000D's 84 GB at Q4_K_M (about 9.8 GB needed), running at an estimated 113 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does Qwen3 14B need?

About 8 GB of weights at Q4_K_M, 16 GB at Q8_0 and 28 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen3 14B on the RTX 6000D?

Roughly 113 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.