Phi 3 Mini 3.8B needs ~9.7 GB but RX 5600 XT 6GB only has 6.0 GB. Try a smaller quantization or lighter model.
Operating mode
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
3.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
17.7 tok/s
TTFT
10929 ms
Safe context
6K
Memory
9.7 GB / 6.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 9.7 GB, but this setup only exposes 6.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Very compromised (needs ~0.3 GB host RAM) | 37.8 tok/s | 2791 ms | 6K |
| Coding | F | Too heavy | 17.7 tok/s | 10929 ms | 6K |
| Agentic Coding | F | Too heavy | 9.7 tok/s | 29058 ms | 6K |
| Reasoning | F | Too heavy | 17.7 tok/s | 12916 ms | 6K |
| RAG | F | Too heavy | 9.7 tok/s | 36322 ms | 6K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | A71 |
Q3_K_S | 3 | 1.9 GB | Low | A72 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$329 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$349 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$449 MSRP
No, Phi 3 Mini 3.8B requires more memory than RX 5600 XT 6GB provides.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.
On RX 5600 XT 6GB, Phi 3 Mini 3.8B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10929ms using Q4_K_M quantization.
For coding workloads, Phi 3 Mini 3.8B on RX 5600 XT 6GB receives a F grade with 17.7 tok/s and 6K context.
On RX 5600 XT 6GB, Phi 3 Mini 3.8B can safely use up to 6K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3-mini-3.8b-on-rx-5600-xt-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A72 |
Q4_K_M | 4 | 2.3 GB | Medium | A71 |
Q5_K_M | 5 | 2.7 GB | High | A71 |
Q6_KBest for your GPU | 6 | 3.1 GB | High | A71 |
Q8_0 | 8 | 4.1 GB | Very High | F0 |
F16 | 16 | 7.8 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
