Can the RTX 6000D run dolphin-2.9.1-yi-1.5-34b?

Yes. dolphin-2.9.1-yi-1.5-34b fits the RTX 6000D's 84 GB at Q4_K_M (about 21.8 GB needed), running at an estimated 45 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
84 GB
Needs at Q4
21.8 GB
Headroom
62.2 GB
Speed (Q4_K_M)
~45 tok/s

Every quantization

dolphin-2.9.1-yi-1.5-34b (34.4B) on 84 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M20 GB21.8 GBFits easily~45 tok/s
Q5_K_M24 GB25.8 GBFits easily~38 tok/s
Q8_037 GB38.8 GBFits easily~24 tok/s
FP1669 GB70.8 GBFits~13 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

llama-server -hf mradermacher/dolphin-2.9.1-yi-1.5-34b-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full dolphin-2.9.1-yi-1.5-34b details →

Compare

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

dolphin-2.9.1-yi-1.5-34b on other hardware

Also runs on the RTX 6000D

Frequently asked

Quick answers

Can the RTX 6000D run dolphin-2.9.1-yi-1.5-34b?

Yes. dolphin-2.9.1-yi-1.5-34b fits the RTX 6000D's 84 GB at Q4_K_M (about 21.8 GB needed), running at an estimated 45 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does dolphin-2.9.1-yi-1.5-34b need?

About 20 GB of weights at Q4_K_M, 37 GB at Q8_0 and 69 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is dolphin-2.9.1-yi-1.5-34b on the RTX 6000D?

Roughly 45 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.