Phi 3.5 Mini 4B needs ~10.8 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~64 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 well
Decode
64.0 tok/s
TTFT
3025 ms
Safe context
30K
Memory
10.8 GB / 16.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 64.0 tok/s | 1650 ms | 30K |
| Coding | A | Runs well | 64.0 tok/s | 3025 ms | 30K |
| Agentic Coding | B | Runs with offload (needs ~0.1 GB host RAM) | 64.0 tok/s | 4400 ms | 30K |
| Reasoning | A | Runs well | 64.0 tok/s | 3575 ms | 30K |
| RAG | B | Runs with offload (needs ~0.1 GB host RAM) | 64.0 tok/s | 5500 ms | 30K |
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B62 |
Q3_K_S | 3 | 2.0 GB | Low | B62 |
NVFP4 | 4 | 2.2 GB | Medium | B62 |
Q4_K_M | 4 | 2.4 GB | Medium | B62 |
Q5_K_M | 5 | 2.9 GB | High | B63 |
Q6_K | 6 | 3.3 GB | High | B63 |
Q8_0 | 8 | 4.3 GB | Very High | B64 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | B67 |
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 115.5 tok/s | |
| Qwen 3 14B | 14B | S | 88.4 tok/s | |
| Qwen 3 8B | 8B | S | 128 tok/s | |
 Phi-4-reasoning-plus 14B | 14.7B | S | 75.5 tok/s | |
 GPT-OSS 20B | 21B | A | 63.6 tok/s |
Yes, RTX 4080 Super 16GB can run Phi 3.5 Mini 4B with a A grade (Runs well). Expected decode speed: 64.0 tok/s.
Phi 3.5 Mini 4B (4B parameters) requires approximately 10.8 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 RTX 4080 Super 16GB, Phi 3.5 Mini 4B achieves approximately 64.0 tokens per second decode speed with a time-to-first-token of 3025ms using Q4_K_M quantization.
For coding workloads, Phi 3.5 Mini 4B on RTX 4080 Super 16GB receives a A grade with 64.0 tok/s and 30K context.
On RTX 4080 Super 16GB, Phi 3.5 Mini 4B can safely use up to 30K 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-4080-super-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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