Can Qwen 2.5 14B run on RX 6900 XT 16GB?
YES — Tight Fit
Qwen 2.5 14B needs ~14.0 GB VRAM. RX 6900 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
36.9 tok/s
TTFT
5246 ms
Safe context
27K
Memory
14.0 GB / 16.0 GB
Memory breakdown
See how fast it feels
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 36.9 tok/s | 2861 ms | 27K |
| Coding | A | Tight fit | 36.9 tok/s | 5246 ms | 27K |
| Agentic Coding | A | Runs with offload (needs ~0.5 GB host RAM) | 24.7 tok/s | 11414 ms | 27K |
| Reasoning | A | Tight fit | 36.9 tok/s | 6199 ms | 27K |
| RAG | A | Runs with offload (needs ~0.5 GB host RAM) | 24.7 tok/s | 14268 ms | 27K |
Quantization options
How Qwen 2.5 14B (14B params) fits at each quantization level on RX 6900 XT 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A80 |
Q3_K_S | 3 | 6.9 GB | Low | A82 |
NVFP4 | 4 | 7.8 GB | Medium | A82 |
Q4_K_M | 4 | 8.5 GB | Medium | A82 |
Q5_K_M | 5 | 10.1 GB | High | A82 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | A82 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen 2.5 14B on your machine.
Run
ollama run qwen2.5Your hardware
More models your RX 6900 XT 16GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 👁 Microsoft Phi-4-reasoning-plus 14B | 14.7B | S | 35 tok/s | |
| 👁 OpenAI GPT-OSS 20B | 21B | A | 33.8 tok/s | |
| 👁 Mistral Codestral 2 25.08 | 22B | A | 12.3 tok/s | |
| 👁 Tsinghua/Zhipu CogVLM2 19B | 19B | A | 19 tok/s |
