Phi-4 14B needs ~25.3 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~196 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
196.0 tok/s
TTFT
988 ms
Safe context
16K
Memory
25.3 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 | 196.0 tok/s | 539 ms | 16K |
| Coding | A | Runs well | 196.0 tok/s | 988 ms | 16K |
| Agentic Coding | A | Runs well | 196.0 tok/s | 1437 ms | 16K |
| Reasoning | A | Runs well | 196.0 tok/s | 1167 ms | 16K |
| RAG | A | Runs well | 196.0 tok/s | 1796 ms | 16K |
How Phi-4 14B (14B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B70 |
Q3_K_S | 3 | 6.9 GB | Low | B70 |
NVFP4 | 4 |
Copy-paste commands to run Phi-4 14B on your machine.
Run
ollama run phi4Your 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 14B with a A grade (Runs well). Expected decode speed: 196.0 tok/s.
Phi-4 14B (14B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 14B is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Phi-4 14B achieves approximately 196.0 tokens per second decode speed with a time-to-first-token of 988ms using Q4_K_M quantization.
For coding workloads, Phi-4 14B on Gaudi 3 128GB receives a A grade with 196.0 tok/s and 16K context.
On Gaudi 3 128GB, Phi-4 14B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-4-14b-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:
| Medium |
| B70 |
Q4_K_M | 4 | 8.5 GB | Medium | B70 |
Q5_K_M | 5 | 10.1 GB | High | B70 |
Q6_K | 6 | 11.5 GB | High | A70 |
Q8_0 | 8 | 15.0 GB | Very High | A70 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | A72 |
| 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 |
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.