Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)
B
Adds memory headroom for longer context windows and future model growth.53.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Can Phi 3 Mini 3.8B run on AMD Instinct MI250 128GB?
YES — Runs Great
B61Good
○Estimated from fit model
Phi 3 Mini 3.8B needs ~21.9 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~53 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 21.9 GB, 53.2 tok/s, Runs well
21.9 GB required128.0 GB available
17% VRAM used
Fit status
Runs well
Decode
53.2 tok/s
TTFT
3639 ms
Safe context
128K
Memory
21.9 GB / 128.0 GB
Memory breakdown
Weights2.3 GB
KV Cache5.9 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsPhi 3 Mini 3.8B on AMD Instinct MI250 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 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 | 53.2 tok/s | 1985 ms | 128K |
| Coding | B | Runs well | 53.2 tok/s | 3639 ms | 128K |
| Agentic Coding | B | Runs well | 53.2 tok/s | 5293 ms | 128K |
| Reasoning | B | Runs well | 53.2 tok/s | 4301 ms | 128K |
| RAG | B | Runs well | 53.2 tok/s | 6617 ms | 128K |
Quantization options
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B55 |
Q3_K_S | 3 | 1.9 GB | Low | B55 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniUpgrade options