Can the RTX 5070 run humanizer?
Yes. humanizer fits the RTX 5070's 12 GB at Q4_K_M (about 8.8 GB needed), running at an estimated 55 tok/s. There's room for up to Q5_K_M if you want higher quality.
Every quantization
humanizer (12B) on 12 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 7 GB | 8.8 GB | Fits | ~55 tok/s |
| Q5_K_M | 9 GB | 10.8 GB | Tight | ~43 tok/s |
| Q8_0 | 13 GB | 14.8 GB | Too big | - |
| FP16 | 24 GB | 25.8 GB | Too big | - |
Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 5070's 672 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 99Ollama 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
- RTX 5090~147 tok/s
- RTX 5090 D~147 tok/s
- RTX 6000D~129 tok/s
- RTX 4090~89 tok/s
- Radeon RX 7900 XTX~79 tok/s
- RTX 5080~79 tok/s
- RTX 3090~78 tok/s
- RTX 3080 Ti~75 tok/s
Also runs on the RTX 5070
- Qwen3 14B~48 tok/s
- Ornith-1.5-9B~64 tok/s
- Ornith-1.5-9B-OBLITERATED~64 tok/s
- Qwen3.5 9B~64 tok/s
- Llama 3.1 8B~70 tok/s
- Qwen3.5 4B~128 tok/s
- Qwen3-4B-Instruct-2507~193 tok/s
- Phi-4 Mini~154 tok/s
Frequently asked
Quick answers
Can the NVIDIA RTX 5070 run humanizer?
Yes. humanizer fits the RTX 5070's 12 GB at Q4_K_M (about 8.8 GB needed), running at an estimated 55 tok/s. There's room for up to Q5_K_M 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 5070?
Roughly 55 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5070's 672 GB/s memory bandwidth.