RTX PRO 5000 Blackwell 48GBBudget pick1344 GB/s (+544)
B
Raises estimated decode speed by about 143%.142.1 tok/s decode
Raises estimated decode speed by about 143%.
~$4,999 MSRP
Phi 3 Medium 14B needs ~19.4 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~58 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
58.4 tok/s
TTFT
3315 ms
Safe context
128K
Memory
19.4 GB / 46.1 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 58.4 tok/s | 1808 ms | 128K |
| Coding | B | Runs well | 58.4 tok/s | 3315 ms | 128K |
| Agentic Coding | B | Runs well | 58.4 tok/s | 4821 ms | 128K |
| Reasoning | B | Runs well | 58.4 tok/s | 3917 ms | 128K |
| RAG | B | Runs well | 58.4 tok/s | 6027 ms | 128K |
How Phi 3 Medium 14B (14B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C53 |
Q3_K_S | 3 | 6.9 GB | Low | C53 |
NVFP4 | 4 | 7.8 GB | Medium | C53 |
Q4_K_M | 4 | 8.5 GB | Medium | C54 |
Q5_K_M | 5 | 10.1 GB | High | C54 |
Q6_K | 6 | 11.5 GB | High | C55 |
Q8_0 | 8 | 15.0 GB | Very High | B56 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | B59 |
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumUpgrade options
RTX PRO 5000 Blackwell 48GBBudget pickRaises estimated decode speed by about 143%.
~$4,999 MSRP
NVIDIA L40 48GBBest valueRaises estimated decode speed by about 53%.
~$5,500 MSRP
Yes, Mac Studio M2 Ultra 64GB can run Phi 3 Medium 14B with a B grade (Runs well). Expected decode speed: 58.4 tok/s.
Phi 3 Medium 14B (14B parameters) requires approximately 19.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Medium 14B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 64GB, Phi 3 Medium 14B achieves approximately 58.4 tokens per second decode speed with a time-to-first-token of 3315ms using Q4_K_M quantization.
For coding workloads, Phi 3 Medium 14B on Mac Studio M2 Ultra 64GB receives a B grade with 58.4 tok/s and 128K context.
On Mac Studio M2 Ultra 64GB, Phi 3 Medium 14B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. Mac Studio M2 Ultra 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3-medium-14b-on-m2-ultra-64gb" 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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