Phi 3.5 Mini 4B needs ~9.4 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q2_K quantization, expect ~56 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
2.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
44.6 tok/s
TTFT
4341 ms
Safe context
10K
Memory
10.3 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 0.2 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 | 56.0 tok/s | 1886 ms | 10K |
| Coding | F | Too heavy | 44.6 tok/s | 4341 ms | 10K |
| Agentic Coding | F | Too heavy | 16.6 tok/s | 16975 ms | 10K |
| Reasoning | F | Too heavy | 44.6 tok/s | 5130 ms | 10K |
| RAG | F | Too heavy | 16.6 tok/s | 21219 ms | 10K |
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B67 |
Q3_K_S | 3 | 2.0 GB | Low | B67 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5Upgrade options
RTX 3060 12GBBudget pickMakes 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
RTX 5060 Ti 16GBBest valueMakes 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
RTX 4060 Ti 16GBNVIDIA upgradeMakes 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.
~$499 MSRP
Yes, RTX 2060 Super 8GB can run Phi 3.5 Mini 4B at Q2_K quantization (Very compromised (needs ~0.2 GB host RAM)). The recommended Q4_K_M requires 10.3 GB which exceeds available memory, but at Q2_K it needs only 9.4 GB. Expected decode speed: 56.0 tok/s.
Phi 3.5 Mini 4B (4B parameters) requires approximately 10.3 GB at Q4_K_M quantization. On RTX 2060 Super 8GB, it fits at Q2_K using 9.4 GB.
The recommended quantization is Q4_K_M, but on RTX 2060 Super 8GB the best fitting quantization is Q2_K, which uses 9.4 GB.
On RTX 2060 Super 8GB, Phi 3.5 Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q2_K quantization.
For coding workloads, Phi 3.5 Mini 4B on RTX 2060 Super 8GB receives a F grade with 44.6 tok/s and 10K context.
On RTX 2060 Super 8GB, Phi 3.5 Mini 4B can safely use up to 12K tokens of context at Q2_K 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-3.5-mini-4b-on-rtx-2060-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.2 GB |
| Medium |
| B68 |
Q4_K_M | 4 | 2.4 GB | Medium | B68 |
Q5_K_M | 5 | 2.9 GB | High | B69 |
Q6_K | 6 | 3.3 GB | High | B69 |
Q8_0Best for your GPU | 8 | 4.3 GB | Very High | B69 |
F16 | 16 | 8.2 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.