Can Phi-4 14B run on RX 6800 16GB?
YES — Tight Fit
Phi-4 14B needs ~14.1 GB VRAM. RX 6800 16GB has 16.0 GB. With Q4_K_M quantization, expect ~35 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
35.4 tok/s
TTFT
5462 ms
Safe context
16K
Memory
14.1 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 | S | Runs well | 35.4 tok/s | 2979 ms | 16K |
| Coding | A | Tight fit | 35.4 tok/s | 5462 ms | 16K |
| Agentic Coding | A | Runs with offload (needs ~0.6 GB host RAM) | 23.0 tok/s | 12249 ms | 16K |
| Reasoning | A | Tight fit | 35.4 tok/s | 6455 ms | 16K |
| RAG | A | Runs with offload (needs ~0.6 GB host RAM) | 23.0 tok/s | 15311 ms | 16K |
Quantization options
How Phi-4 14B (14B params) fits at each quantization level on RX 6800 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A81 |
Q3_K_S | 3 | 6.9 GB | Low | A82 |
NVFP4 | 4 | 7.8 GB | Medium | A83 |
Q4_K_M | 4 | 8.5 GB | Medium | A83 |
Q5_K_M | 5 | 10.1 GB | High | A83 |
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 Phi-4 14B on your machine.
Run
ollama run phi4Your hardware
More models your RX 6800 16GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 👁 Microsoft Phi-4-reasoning-plus 14B | 14.7B | S | 33.8 tok/s | |
| 👁 OpenAI GPT-OSS 20B | 21B | A | 32.6 tok/s | |
| 👁 Mistral Codestral 2 25.08 | 22B | A | 11.9 tok/s | |
| 👁 Tsinghua/Zhipu CogVLM2 19B | 19B | A | 18.3 tok/s |
