RX 7900 XTX 24GBコスパ重視
960 GB/s (+560)
B
Raises estimated decode speed by about 198%.87 tok/sデコード
Raises estimated decode speed by about 198%.
〜$999 MSRP
Can Phi 3 Medium 14B run on MacBook Pro M2 Max 32GB?
YES — Runs Great
B64Good
Estimated from fit model
Phi 3 Medium 14B needs ~15.9 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~29 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 15.9 GB, 29.2 tok/s, Runs well
15.9 GB required23.0 GB available
69% VRAM used
Fit status
Runs well
Decode
29.2 tok/s
TTFT
6629 ms
Safe context
53K
Memory
15.9 GB / 23.0 GB
Memory breakdown
Weights8.5 GB
KV Cache3.1 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsPhi 3 Medium 14B on MacBook Pro M2 Max 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 29.2 tok/s decode · 6.6s TTFT (warm) · 73 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 29.2 tok/s | 3616 ms | 53K |
| Coding | B | Runs well | 29.2 tok/s | 6629 ms | 53K |
| Agentic Coding | B | Tight fit | 29.2 tok/s | 9643 ms | 53K |
| Reasoning | B | Runs well | 29.2 tok/s | 7835 ms | 53K |
| RAG | B | Tight fit | 29.2 tok/s | 12053 ms | 53K |
Quantization options
How Phi 3 Medium 14B (14B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B57 |
Q3_K_S | 3 | 6.9 GB | Low | B58 |
NVFP4 | 4 | 7.8 GB | Medium | B59 |
Q4_K_M | 4 | 8.5 GB | Medium | B59 |
Q5_K_M | 5 | 10.1 GB | High | B60 |
Q6_K | 6 | 11.5 GB | High | B61 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | B61 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumアップグレードオプション
Phi 3 Medium 14Bを快適に動かすハードウェア
Frequently asked questions
Can MacBook Pro M2 Max 32GB run Phi 3 Medium 14B?
Yes, MacBook Pro M2 Max 32GB can run Phi 3 Medium 14B with a B grade (Runs well). Expected decode speed: 29.2 tok/s.
How much VRAM does Phi 3 Medium 14B need?
Phi 3 Medium 14B (14B parameters) requires approximately 15.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Phi 3 Medium 14B?
The recommended quantization for Phi 3 Medium 14B is Q4_K_M, which balances quality and memory efficiency.
What speed will Phi 3 Medium 14B run at on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, Phi 3 Medium 14B achieves approximately 29.2 tokens per second decode speed with a time-to-first-token of 6629ms using Q4_K_M quantization.
Can MacBook Pro M2 Max 32GB run Phi 3 Medium 14B for coding?
For coding workloads, Phi 3 Medium 14B on MacBook Pro M2 Max 32GB receives a B grade with 29.2 tok/s and 53K context.
What context window can Phi 3 Medium 14B use on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, Phi 3 Medium 14B can safely use up to 53K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for Phi 3 Medium 14B?
Not always. MacBook Pro M2 Max 32GB 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.
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