MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+12)
B
This setup is broadly balanced for this model.53.2 tok/s decode
~$2,499 MSRP
Phi 3 Mini 3.8B needs ~11.5 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~61 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
60.8 tok/s
TTFT
3184 ms
Safe context
50K
Memory
11.5 GB / 24.0 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 | 60.8 tok/s | 1737 ms | 50K |
| Coding | B | Runs well | 60.8 tok/s | 3184 ms | 50K |
| Agentic Coding | A | Runs well | 60.8 tok/s | 4632 ms | 50K |
| Reasoning | B | Runs well | 60.8 tok/s | 3763 ms | 50K |
| RAG | A | Runs well | 60.8 tok/s | 5789 ms | 50K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B61 |
Q3_K_S | 3 | 1.9 GB | Low | B61 |
NVFP4 | 4 | 2.1 GB | Medium | B61 |
Q4_K_M | 4 | 2.3 GB | Medium | B61 |
Q5_K_M | 5 | 2.7 GB | High | B61 |
Q6_K | 6 | 3.1 GB | High | B62 |
Q8_0 | 8 | 4.1 GB | Very High | B62 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B64 |
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniUpgrade options
Yes, RTX 4090 24GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 60.8 tok/s.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 11.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4090 24GB, Phi 3 Mini 3.8B achieves approximately 60.8 tokens per second decode speed with a time-to-first-token of 3184ms using Q4_K_M quantization.
For coding workloads, Phi 3 Mini 3.8B on RTX 4090 24GB receives a B grade with 60.8 tok/s and 50K context.
On RTX 4090 24GB, Phi 3 Mini 3.8B can safely use up to 50K 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-mini-3.8b-on-rtx-4090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: