Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
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VOOZH | about |
Phi 3 Medium 14B needs ~15.1 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~8 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
Tight fit
Decode
8.2 tok/s
TTFT
23661 ms
Safe context
28K
Memory
15.1 GB / 17.3 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 7.6 tok/s | 13874 ms | 28K |
| Coding | B | Tight fit | 7.6 tok/s | 25436 ms | 28K |
| Agentic Coding | B | Runs with offload | 6.9 tok/s | 40518 ms | 28K |
| Reasoning | B | Tight fit | 7.6 tok/s | 30061 ms | 28K |
| RAG | B | Runs with offload | 6.9 tok/s | 50648 ms | 28K |
How Phi 3 Medium 14B (14B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B60 |
Q3_K_S | 3 | 6.9 GB | Low | B61 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 666%.
~$2,000 MSRP
Raises estimated decode speed by about 612%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
7.8 GB |
| Medium |
| B62 |
Q4_K_M | 4 | 8.5 GB | Medium | B62 |
Q5_K_M | 5 | 10.1 GB | High | B62 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | B62 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.