Can the RTX 6000D run humanizer?

Yes. humanizer fits the RTX 6000D's 84 GB at Q4_K_M (about 8.8 GB needed), running at an estimated 129 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
84 GB
Needs at Q4
8.8 GB
Headroom
75.2 GB
Speed (Q4_K_M)
~129 tok/s

Every quantization

humanizer (12B) on 84 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M7 GB8.8 GBFits easily~129 tok/s
Q5_K_M9 GB10.8 GBFits easily~100 tok/s
Q8_013 GB14.8 GBFits easily~69 tok/s
FP1624 GB25.8 GBFits easily~38 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 jialinyyzz/humanizer-GGUF -ngl 99

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

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humanizer on other hardware

Also runs on the RTX 6000D

Frequently asked

Quick answers

Can the RTX 6000D run humanizer?

Yes. humanizer fits the RTX 6000D's 84 GB at Q4_K_M (about 8.8 GB needed), running at an estimated 129 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does humanizer need?

About 7 GB of weights at Q4_K_M, 13 GB at Q8_0 and 24 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is humanizer on the RTX 6000D?

Roughly 129 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.