Can the RTX 5090 run humanizer?

Yes. humanizer fits the RTX 5090's 32 GB at Q4_K_M (about 8.8 GB needed), running at an estimated 147 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
32 GB
Needs at Q4
8.8 GB
Headroom
23.2 GB
Speed (Q4_K_M)
~147 tok/s

Every quantization

humanizer (12B) on 32 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M7 GB8.8 GBFits easily~147 tok/s
Q5_K_M9 GB10.8 GBFits easily~114 tok/s
Q8_013 GB14.8 GBFits easily~79 tok/s
FP1624 GB25.8 GBFits~43 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 5090's 1792 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 →

Compare

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

humanizer on other hardware

Also runs on the RTX 5090

Frequently asked

Quick answers

Can the NVIDIA RTX 5090 run humanizer?

Yes. humanizer fits the RTX 5090's 32 GB at Q4_K_M (about 8.8 GB needed), running at an estimated 147 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 5090?

Roughly 147 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5090's 1792 GB/s memory bandwidth.