Phi-4 Mini Reasoning 4B needs ~17.5 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~53 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
53.2 tok/s
TTFT
3639 ms
Safe context
131K
Memory
17.5 GB / 128.0 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 53.2 tok/s | 1985 ms | 131K |
| Coding | A | Runs well | 53.2 tok/s | 3639 ms | 131K |
| Agentic Coding | A | Runs well | 53.2 tok/s | 5293 ms | 131K |
| Reasoning | A | Runs well | 53.2 tok/s | 4301 ms | 131K |
| RAG | A | Runs well | 53.2 tok/s | 6617 ms | 131K |
How Phi-4 Mini Reasoning 4B (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 | A75 |
Q3_K_S | 3 | 1.9 GB | Low | A75 |
NVFP4 | 4 |
Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.
Run
ollama run phi4-miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 37.5 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, Gaudi 3 128GB can run Phi-4 Mini Reasoning 4B with a A grade (Runs well). Expected decode speed: 53.2 tok/s.
Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 17.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Phi-4 Mini Reasoning 4B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.
For coding workloads, Phi-4 Mini Reasoning 4B on Gaudi 3 128GB receives a A grade with 53.2 tok/s and 131K context.
On Gaudi 3 128GB, Phi-4 Mini Reasoning 4B can safely use up to 131K tokens of context. The model's official context limit is 131K, 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-4-mini-reasoning-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>
Preview:
2.1 GB |
| Medium |
| A75 |
Q4_K_M | 4 | 2.3 GB | Medium | A75 |
Q5_K_M | 5 | 2.7 GB | High | A75 |
Q6_K | 6 | 3.1 GB | High | A75 |
Q8_0 | 8 | 4.1 GB | Very High | A75 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | A75 |
| 391.6 tok/s |
| Qwen 3.5 27B | 27B | S | 169.8 tok/s |
| Qwen 3.6 27B | 27B | S | 105.9 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 104.1 tok/s |
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.
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.