Can the RTX 5080 run humanizer?

Yes. humanizer fits the RTX 5080's 16 GB at Q4_K_M (about 8.8 GB needed), running at an estimated 79 tok/s. There's room for up to Q8_0 if you want higher quality.

VRAM
16 GB
Needs at Q4
8.8 GB
Headroom
7.2 GB
Speed (Q4_K_M)
~79 tok/s

Every quantization

humanizer (12B) on 16 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M7 GB8.8 GBFits easily~79 tok/s
Q5_K_M9 GB10.8 GBFits~61 tok/s
Q8_013 GB14.8 GBTight~42 tok/s
FP1624 GB25.8 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 5080's 960 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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Same model, other hardware - and other models on this card

humanizer on other hardware

Also runs on the RTX 5080

Frequently asked

Quick answers

Can the NVIDIA RTX 5080 run humanizer?

Yes. humanizer fits the RTX 5080's 16 GB at Q4_K_M (about 8.8 GB needed), running at an estimated 79 tok/s. There's room for up to Q8_0 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 5080?

Roughly 79 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5080's 960 GB/s memory bandwidth.