Can Phi-4-reasoning-plus 14B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

A82Great

Estimated from fit model

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.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.3 GB, 19.6 tok/s, Runs well

26.3 GB required108.8 GB available

24% VRAM used

Fit status

Runs well

Decode

19.6 tok/s

TTFT

9859 ms

Safe context

33K

Memory

26.3 GB / 108.8 GB

Memory breakdown

Weights9.0 GB

KV Cache3.1 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsPhi-4-reasoning-plus 14B on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 19.6 tok/s decode · 9.9s TTFT (warm) · 49 tok/s prefill

What limits this setup

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.

Best improvement path

Performance by workload

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

Inference speed

Phi-4-reasoning-plus 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi-4-reasoning-plus 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~144 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	143.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	91.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	82.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	78.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	75.5	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	66.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	55.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	52.7	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	37.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	37.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	28.8	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	26.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	24.9	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	14.6	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	5.5	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

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	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

Get started

Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.

Run

ollama run phi4-reasoning

Your hardware

More models your NVIDIA DGX Spark 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	2.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	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

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Phi-4-reasoning-plus 14B?

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.

How much VRAM does Phi-4-reasoning-plus 14B need?

Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4-reasoning-plus 14B?

The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4-reasoning-plus 14B run at on NVIDIA DGX Spark 128GB?

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.

Can NVIDIA DGX Spark 128GB run Phi-4-reasoning-plus 14B for coding?

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.

What context window can Phi-4-reasoning-plus 14B use on NVIDIA DGX Spark 128GB?

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.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Phi-4-reasoning-plus 14B?

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.

See all results for NVIDIA DGX Spark 128GB See all hardware for Phi-4-reasoning-plus 14B

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