Phi 3.5 Mini 4B needs ~9.8 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
3.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
16.4 tok/s
TTFT
11811 ms
Safe context
6K
Memory
9.8 GB / 6.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 9.8 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) | 34.6 tok/s | 3050 ms | 6K |
| Coding | F | Too heavy | 16.4 tok/s | 11811 ms | 6K |
| Agentic Coding | F | Too heavy | 9.2 tok/s | 30587 ms | 6K |
| Reasoning | F | Too heavy | 16.4 tok/s | 13959 ms | 6K |
| RAG | F | Too heavy | 9.2 tok/s | 38234 ms | 6K |
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B70 |
Q3_K_S | 3 | 2.0 GB | Low | A70 |
NVFP4 | 4 | 2.2 GB | Medium | A70 |
Q4_K_M | 4 | 2.4 GB | Medium | A70 |
Q5_K_M | 5 | 2.9 GB | High | B70 |
Q6_KBest for your GPU | 6 | 3.3 GB | High | B69 |
Q8_0 | 8 | 4.3 GB | Very High | F0 |
F16 | 16 | 8.2 GB | Maximum | F0 |
アップグレードオプション
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 3.5 Mini 4B requires more memory than RX 5600 XT 6GB provides.
Phi 3.5 Mini 4B (4B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On RX 5600 XT 6GB, Phi 3.5 Mini 4B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11811ms using Q4_K_M quantization.
For coding workloads, Phi 3.5 Mini 4B on RX 5600 XT 6GB receives a F grade with 16.4 tok/s and 6K context.
On RX 5600 XT 6GB, Phi 3.5 Mini 4B can safely use up to 6K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3.5-mini-4b-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:
