Mac Studio M3 Ultra 256GBコスパ重視
256 GB Unified (+128)
B
Adds memory headroom for longer context windows and future model growth.53.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$6,999 MSRP
Can Phi 3 Mini 3.8B run on Gaudi 3 128GB?
YES — Runs Great
B61Good
Estimated from fit model
Phi 3 Mini 3.8B needs ~21.9 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~53 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 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 Gaudi 3 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
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Best improvement path
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade 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 Gaudi 3 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 | 2.1 GB | Medium | B55 |
Q4_K_M | 4 | 2.3 GB | Medium | B55 |
Q5_K_M | 5 | 2.7 GB | High | B55 |
Q6_K | 6 | 3.1 GB | High | B55 |
Q8_0 | 8 | 4.1 GB | Very High | B55 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B55 |
Get started
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniアップグレードオプション
Phi 3 Mini 3.8Bを快適に動かすハードウェア
Frequently asked questions
Can Gaudi 3 128GB run Phi 3 Mini 3.8B?
Yes, Gaudi 3 128GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 53.2 tok/s.
How much VRAM does Phi 3 Mini 3.8B need?
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 21.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Phi 3 Mini 3.8B?
The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Phi 3 Mini 3.8B run at on Gaudi 3 128GB?
On Gaudi 3 128GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.
Can Gaudi 3 128GB run Phi 3 Mini 3.8B for coding?
For coding workloads, Phi 3 Mini 3.8B on Gaudi 3 128GB receives a B grade with 53.2 tok/s and 128K context.
What context window can Phi 3 Mini 3.8B use on Gaudi 3 128GB?
On Gaudi 3 128GB, Phi 3 Mini 3.8B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
What should I upgrade first if Phi 3 Mini 3.8B feels slow on Gaudi 3 128GB?
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Would CUDA be a better path than Gaudi 3 128GB for Phi 3 Mini 3.8B?
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
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<iframe src="https://willitrunai.com/embed/phi-3-mini-3.8b-on-gaudi-3-128gb" 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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