Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Phi 3 Mini 3.8B?

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.

Can Phi 3 Mini 3.8B run on NVIDIA DGX Spark 128GB?

YES — With F16

B61Good

Estimated from fit model

Phi 3 Mini 3.8B needs ~27.9 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~29 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.

Phi 3 Mini 3.8B at Q4_K_M needs 9.4 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (27.9 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.4 GB, 53.2 tok/s, Runs well

22.4 GB required108.8 GB available

21% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

128K

Memory

22.4 GB / 108.8 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B on NVIDIA DGX Spark 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

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	F	Too heavy	12.7 tok/s	8302 ms	4K
Coding	F	Too heavy	12.7 tok/s	15221 ms	4K
Agentic Coding	F	Too heavy	12.7 tok/s	22139 ms	4K
Reasoning	F	Too heavy	12.7 tok/s	17988 ms	4K
RAG	F	Too heavy	12.7 tok/s	27674 ms	4K

Inference speed

Phi 3 Mini 3.8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 3 Mini 3.8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~72 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	72.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	60.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	60.8

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B56
Q3_K_S	3	1.9 GB	Low	B56
NVFP4	4

Get started

Copy-paste commands to run Phi 3 Mini 3.8B on your machine.

Run

ollama run phi3:mini

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Phi 3 Mini 3.8B?

Yes, NVIDIA DGX Spark 128GB can run Phi 3 Mini 3.8B at F16 quantization (Runs well). The recommended Q4_K_M requires 9.4 GB which exceeds available memory, but at F16 it needs only 27.9 GB. Expected decode speed: 29.4 tok/s.

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 9.4 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 27.9 GB.

What is the best quantization for Phi 3 Mini 3.8B?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 27.9 GB.

What speed will Phi 3 Mini 3.8B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Phi 3 Mini 3.8B achieves approximately 29.4 tokens per second decode speed with a time-to-first-token of 6577ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on NVIDIA DGX Spark 128GB receives a F grade with 12.7 tok/s and 4K context.

What context window can Phi 3 Mini 3.8B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Phi 3 Mini 3.8B can safely use up to 128K tokens of context at F16 quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA DGX Spark 128GB See all hardware for Phi 3 Mini 3.8B

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