Phi-4-reasoning-plus 14B needs ~26.3 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~20 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
19.6 tok/s
TTFT
9859 ms
Safe context
33K
Memory
26.3 GB / 108.8 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 19.6 tok/s | 5378 ms | 33K |
| Coding | A | Runs well | 19.6 tok/s | 9859 ms | 33K |
| Agentic Coding | A | Runs well | 19.6 tok/s | 14340 ms | 33K |
| Reasoning | A | Runs well | 19.6 tok/s | 11651 ms | 33K |
| RAG | A | Runs well | 19.6 tok/s | 17925 ms | 33K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | A79 |
Q3_K_S | 3 | 7.2 GB | Low | A79 |
NVFP4 | 4 |
Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.
Run
ollama run phi4-reasoningYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 2.4 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, NVIDIA DGX Spark 128GB can run Phi-4-reasoning-plus 14B with a A grade (Runs well). Expected decode speed: 19.6 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Phi-4-reasoning-plus 14B achieves approximately 19.6 tokens per second decode speed with a time-to-first-token of 9859ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on NVIDIA DGX Spark 128GB receives a A grade with 19.6 tok/s and 33K context.
On NVIDIA DGX Spark 128GB, Phi-4-reasoning-plus 14B can safely use up to 33K tokens of context. The model's official context limit is 33K, 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-reasoning-plus-14b-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
8.2 GB |
| Medium |
| A79 |
Q4_K_M | 4 | 9.0 GB | Medium | A79 |
Q5_K_M | 5 | 10.6 GB | High | A79 |
Q6_K | 6 | 12.1 GB | High | A79 |
Q8_0 | 8 | 15.7 GB | Very High | A80 |
F16Best for your GPU | 16 | 30.1 GB | Maximum | A82 |
| 24.8 tok/s |
| Qwen 3.5 27B | 27B | A | 10.7 tok/s |
| Qwen 3.6 27B | 27B | A | 10.8 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 6.6 tok/s |
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.