Can the RTX 6000D run Qwen3.8-27B-OBLITERATED?

Yes. Qwen3.8-27B-OBLITERATED fits the RTX 6000D's 84 GB at Q4_K_M (about 17.8 GB needed), running at an estimated 56 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
84 GB
Needs at Q4
17.8 GB
Headroom
66.2 GB
Speed (Q4_K_M)
~56 tok/s

Every quantization

Qwen3.8-27B-OBLITERATED (27.8B) on 84 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M16 GB17.8 GBFits easily~56 tok/s
Q5_K_M20 GB21.8 GBFits easily~45 tok/s
Q8_030 GB31.8 GBFits easily~30 tok/s
FP1656 GB57.8 GBFits~16 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/Qwen3.8-27B-OBLITERATED-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3.8-27B-OBLITERATED details →

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

Qwen3.8-27B-OBLITERATED on other hardware

Also runs on the RTX 6000D

Frequently asked

Quick answers

Can the RTX 6000D run Qwen3.8-27B-OBLITERATED?

Yes. Qwen3.8-27B-OBLITERATED fits the RTX 6000D's 84 GB at Q4_K_M (about 17.8 GB needed), running at an estimated 56 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does Qwen3.8-27B-OBLITERATED need?

About 16 GB of weights at Q4_K_M, 30 GB at Q8_0 and 56 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen3.8-27B-OBLITERATED on the RTX 6000D?

Roughly 56 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.