MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)546 GB/s (+34)
B
This setup is broadly balanced for this model.38.1 tok/s decode
~$2,499 MSRP
Phi 3 Medium 14B needs ~15.7 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~36 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
36.1 tok/s
TTFT
5364 ms
Safe context
102K
Memory
15.7 GB / 32.0 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 | 36.1 tok/s | 2926 ms | 102K |
| Coding | B | Runs well | 36.1 tok/s | 5364 ms | 102K |
| Agentic Coding | B | Runs well | 36.1 tok/s | 7802 ms | 102K |
| Reasoning | B | Runs well | 36.1 tok/s | 6339 ms | 102K |
| RAG | B | Runs well | 36.1 tok/s | 9753 ms | 102K |
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 Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C55 |
Q3_K_S | 3 | 6.9 GB | Low | B55 |
NVFP4 | 4 | 7.8 GB | Medium | B56 |
Q4_K_M | 4 | 8.5 GB | Medium | B56 |
Q5_K_M | 5 | 10.1 GB | High | B57 |
Q6_K | 6 | 11.5 GB | High | B57 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | B59 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumUpgrade options
~$2,499 MSRP
Raises estimated decode speed by about 62%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Radeon Pro W6800 32GB can run Phi 3 Medium 14B with a B grade (Runs well). Expected decode speed: 36.1 tok/s.
Phi 3 Medium 14B (14B parameters) requires approximately 15.7 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 Radeon Pro W6800 32GB, Phi 3 Medium 14B achieves approximately 36.1 tokens per second decode speed with a time-to-first-token of 5364ms using Q4_K_M quantization.
For coding workloads, Phi 3 Medium 14B on Radeon Pro W6800 32GB receives a B grade with 36.1 tok/s and 102K context.
On Radeon Pro W6800 32GB, Phi 3 Medium 14B can safely use up to 102K 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-medium-14b-on-radeon-pro-w6800-32gb" 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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