MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)546 GB/s (+34)
B
This setup is broadly balanced for this model.38.1 tok/s Dekodierung
ca. $2,499 MSRP
Can Phi 3 Medium 14B run on Radeon Pro W6800 32GB?
YES — Runs Great
B60Good
Estimated from fit model
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.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 15.7 GB, 36.1 tok/s, Runs well
15.7 GB required32.0 GB available
49% VRAM used
Fit status
Runs well
Decode
36.1 tok/s
TTFT
5364 ms
Safe context
102K
Memory
15.7 GB / 32.0 GB
Memory breakdown
Weights8.5 GB
KV Cache3.1 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsPhi 3 Medium 14B on Radeon Pro W6800 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: 36.1 tok/s decode · 5.4s TTFT (warm) · 90 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by 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
Phi 3 Medium 14B inference speed — tokens per second by GPU & Mac
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.
Quantization options
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 |
Get started
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumUpgrade-Optionen
Hardware, die Phi 3 Medium 14B gut ausführt
Frequently asked questions
Can Radeon Pro W6800 32GB run Phi 3 Medium 14B?
Yes, Radeon Pro W6800 32GB can run Phi 3 Medium 14B with a B grade (Runs well). Expected decode speed: 36.1 tok/s.
How much VRAM does Phi 3 Medium 14B need?
Phi 3 Medium 14B (14B parameters) requires approximately 15.7 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 Radeon Pro W6800 32GB?
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.
Can Radeon Pro W6800 32GB run Phi 3 Medium 14B for coding?
For coding workloads, Phi 3 Medium 14B on Radeon Pro W6800 32GB receives a B grade with 36.1 tok/s and 102K context.
What context window can Phi 3 Medium 14B use on Radeon Pro W6800 32GB?
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.
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