Phi 3 Medium 14B needs ~16.4 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~14 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
13.8 tok/s
TTFT
14046 ms
Safe context
66K
Memory
16.4 GB / 25.9 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 | 13.8 tok/s | 7661 ms | 66K |
| Coding | B | Runs well | 13.8 tok/s | 14046 ms | 66K |
| Agentic Coding | B | Runs well | 13.8 tok/s | 20430 ms | 66K |
| Reasoning | B | Runs well | 13.8 tok/s | 16599 ms | 66K |
| RAG | B | Runs well | 13.8 tok/s | 25537 ms | 66K |
Inference speed
Estimated decode speed (tokens/sec) for Phi 3 Medium 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 151.1 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 96.4 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 87.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 82.5 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 80.7 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 70.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 58.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 55.4 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 38.1 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 38.1 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 30.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 28.4 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 27.7 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 23.3 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 16.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 6.1 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Phi 3 Medium 14B (14B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B56 |
Q3_K_S | 3 | 6.9 GB | Low | B57 |
NVFP4 | 4 | 7.8 GB | Medium | B57 |
Q4_K_M | 4 | 8.5 GB | Medium | B58 |
Q5_K_M | 5 | 10.1 GB | High | B59 |
Q6_K | 6 | 11.5 GB | High | B60 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | B61 |
F16 | 16 | 28.7 GB | Maximum | F0 |
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
Raises estimated decode speed by about 176%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Raises estimated decode speed by about 323%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Yes, MacBook Pro M3 Pro 36GB can run Phi 3 Medium 14B with a B grade (Runs well). Expected decode speed: 13.8 tok/s.
Phi 3 Medium 14B (14B parameters) requires approximately 16.4 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 MacBook Pro M3 Pro 36GB, Phi 3 Medium 14B achieves approximately 13.8 tokens per second decode speed with a time-to-first-token of 14046ms using Q4_K_M quantization.
For coding workloads, Phi 3 Medium 14B on MacBook Pro M3 Pro 36GB receives a B grade with 13.8 tok/s and 66K context.
On MacBook Pro M3 Pro 36GB, Phi 3 Medium 14B can safely use up to 66K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 Pro 36GB 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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<iframe src="https://willitrunai.com/embed/phi-3-medium-14b-on-m3-pro-36gb" 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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