MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+12)
B
This setup is broadly balanced for this model.56 tok/s decode
~$2,499 MSRP
Phi 3.5 Mini 4B needs ~11.6 GB VRAM. RTX 4090 24GB has 24.0 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
50K
Memory
11.6 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 | 64.0 tok/s | 1650 ms | 50K |
| Coding | B | Runs well | 64.0 tok/s | 3025 ms | 50K |
| Agentic Coding | A | Runs well | 64.0 tok/s | 4400 ms | 50K |
| Reasoning | B | Runs well | 64.0 tok/s | 3575 ms | 50K |
| RAG | A | Runs well | 64.0 tok/s | 5500 ms | 50K |
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B60 |
Q3_K_S | 3 | 2.0 GB | Low | B60 |
NVFP4 | 4 | 2.2 GB | Medium | B60 |
Q4_K_M | 4 | 2.4 GB | Medium | B60 |
Q5_K_M | 5 | 2.9 GB | High | B60 |
Q6_K | 6 | 3.3 GB | High | B60 |
Q8_0 | 8 | 4.3 GB | Very High | B61 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | B63 |
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5Upgrade options
Yes, RTX 4090 24GB can run Phi 3.5 Mini 4B with a B grade (Runs well). Expected decode speed: 64.0 tok/s.
Phi 3.5 Mini 4B (4B parameters) requires approximately 11.6 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 4090 24GB, 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 4090 24GB receives a B grade with 64.0 tok/s and 50K context.
On RTX 4090 24GB, Phi 3.5 Mini 4B 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-3.5-mini-4b-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>
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