bartowski
Qwen2.5 14B Instruct
Qwen2.5 14B Instruct is a 14.8B dense qwen2 model from bartowski. Configured context length is 32,768 tokens. License: apache-2.0. Quant sizes are the GGUF file bytes published on Hugging Face when a file exists.
Parameters14.8B
Context32K
Attentiongqa
Licenseapache-2.0
48 layers · 8 KV heads · head dim 128 · GGUF repo · config
Quant file sizes
| Quant | File size | Source |
|---|---|---|
| F16 | 27.5 GB | Hugging Face file bytes |
| Q8_0 | 14.6 GB | Hugging Face file bytes |
| Q6_K | 22.9 GB | Hugging Face file bytes |
| Q5_K_M | 9.79 GB | Hugging Face file bytes |
| Q5_K_S | 9.56 GB | Hugging Face file bytes |
| Q4_K_M | 8.37 GB | Hugging Face file bytes |
| Q4_K_S | 7.98 GB | Hugging Face file bytes |
| IQ4_XS | 7.56 GB | Hugging Face file bytes |
| Q4_0 | 31.8 GB | Hugging Face file bytes |
| Q3_K_L | 7.38 GB | Hugging Face file bytes |
| Q3_K_M | 6.84 GB | Hugging Face file bytes |
| IQ3_M | 6.44 GB | Hugging Face file bytes |
| Q3_K_S | 6.20 GB | Hugging Face file bytes |
| Q2_K | 11.5 GB | Hugging Face file bytes |
On common machines
| Device | Verdict | Quant | Speed |
|---|---|---|---|
| GeForce RTX 4060 Ti 8GB | Tight fit | Q3_K_S | 22–31 |
| GeForce RTX 3060 12GB | Tight fit | Q4_K_M | 19–25 |
| GeForce RTX 4060 Ti 16GB | Runs | Q5_K_M | 13–18 |
| GeForce RTX 4090 | Runs | Q8_0 | 32–44 |
| GeForce RTX 5090 | Tight fit | F16 | 32–44 |
| Apple M4 Pro | Tight fit | Q8_0 | 8.7–12 |
| Apple M5 | Tight fit | Q5_K_S | 7.6–10 |
| CPU only · DDR5-5600 | Runs on CPU | Q8_0 | 2.8–3.9 |
Smallest devices that fit it fully
- GeForce RTX 5060 Ti 8GB — Tight fit, Q3_K_S, 35–48 tok/s
- GeForce RTX 5060 — Tight fit, Q3_K_S, 35–48 tok/s
- GeForce RTX 5050 — Tight fit, Q3_K_S, 25–34 tok/s
- GeForce RTX 4060 Ti 8GB — Tight fit, Q3_K_S, 22–31 tok/s
- GeForce RTX 4060 — Tight fit, Q3_K_S, 21–29 tok/s
- GeForce RTX 3070 Ti — Tight fit, Q3_K_S, 47–65 tok/s
Run it
Commands for the GeForce RTX 4060 Ti 16GB estimate (Q5_K_M).
llama.cpp
llama-server -hf bartowski/Qwen2.5-14B-Instruct-GGUF:Q5_K_M -c 8192 -ngl 48
Ollama
ollama run qwen2.5
LM Studio
lms get bartowski/Qwen2.5-14B-Instruct-GGUF # Then load quant Q5_K_M