RTX 5060 Ti 16GBBudget pick16 GB VRAM (+5)
A
Adds memory headroom for longer context windows and future model growth.56 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Phi 3.5 Mini 4B needs ~10.6 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M 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
Fit status
Runs with offload
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
17K
Memory
10.6 GB / 11.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 56.0 tok/s | 1886 ms | 17K |
| Coding | B | Runs with offload | 56.0 tok/s | 3457 ms | 17K |
| Agentic Coding | F | Too heavy | 49.4 tok/s | 5700 ms | 17K |
| Reasoning | B | Runs with offload | 56.0 tok/s | 4086 ms | 17K |
| RAG | F | Too heavy | 49.4 tok/s | 7125 ms | 17K |
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B64 |
Q3_K_S | 3 | 2.0 GB | Low | B64 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5Upgrade options
RTX 5060 Ti 16GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBBest valueAdds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 2000 Ada 16GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$625 MSRP
Yes, RTX 2080 Ti 11GB can run Phi 3.5 Mini 4B with a B grade (Runs with offload). Expected decode speed: 56.0 tok/s.
Phi 3.5 Mini 4B (4B parameters) requires approximately 10.6 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 RTX 2080 Ti 11GB, Phi 3.5 Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
For coding workloads, Phi 3.5 Mini 4B on RTX 2080 Ti 11GB receives a B grade with 56.0 tok/s and 17K context.
On RTX 2080 Ti 11GB, Phi 3.5 Mini 4B can safely use up to 17K 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-3.5-mini-4b-on-rtx-2080-ti-11gb" 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 |
| B65 |
Q4_K_M | 4 | 2.4 GB | Medium | B65 |
Q5_K_M | 5 | 2.9 GB | High | B66 |
Q6_K | 6 | 3.3 GB | High | B66 |
Q8_0Best for your GPU | 8 | 4.3 GB | Very High | B68 |
F16 | 16 | 8.2 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.