Phi 3 Mini 3.8B needs ~9.4 GB VRAM. RX 7600 8GB has 8.0 GB. With Q3_K_S quantization, expect ~44 tok/s.
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
1.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
34.7 tok/s
TTFT
5583 ms
Safe context
11K
Memory
9.9 GB / 8.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 53.2 tok/s | 1985 ms | 11K |
| Coding | F | Too heavy | 34.7 tok/s | 5583 ms | 11K |
| Agentic Coding | F | Too heavy | 13.0 tok/s | 21645 ms | 11K |
| Reasoning | F | Too heavy | 34.7 tok/s | 6598 ms | 11K |
| RAG | F | Too heavy | 13.0 tok/s | 27057 ms | 11K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RX 7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B68 |
Q3_K_S | 3 | 1.9 GB | Low | B69 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniUpgrade 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
Yes, RX 7600 8GB can run Phi 3 Mini 3.8B at Q3_K_S quantization (Very compromised (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 9.9 GB which exceeds available memory, but at Q3_K_S it needs only 9.4 GB. Expected decode speed: 44.3 tok/s.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 9.9 GB at Q4_K_M quantization. On RX 7600 8GB, it fits at Q3_K_S using 9.4 GB.
The recommended quantization is Q4_K_M, but on RX 7600 8GB the best fitting quantization is Q3_K_S, which uses 9.4 GB.
On RX 7600 8GB, Phi 3 Mini 3.8B achieves approximately 44.3 tokens per second decode speed with a time-to-first-token of 4365ms using Q3_K_S quantization.
For coding workloads, Phi 3 Mini 3.8B on RX 7600 8GB receives a F grade with 34.7 tok/s and 11K context.
On RX 7600 8GB, Phi 3 Mini 3.8B can safely use up to 12K tokens of context at Q3_K_S quantization. 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-7600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| B69 |
Q4_K_M | 4 | 2.3 GB | Medium | B69 |
Q5_K_M | 5 | 2.7 GB | High | A70 |
Q6_K | 6 | 3.1 GB | High | A71 |
Q8_0Best for your GPU | 8 | 4.1 GB | Very High | A70 |
F16 | 16 | 7.8 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.