Will It Run AI

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
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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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well53.2 tok/s1985 ms128K
CodingFToo heavy12.7 tok/s15221 ms4K
Agentic CodingBRuns well53.2 tok/s5293 ms128K
ReasoningBRuns well53.2 tok/s4301 ms128K
RAGBRuns well53.2 tok/s6617 ms128K

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).

QuantBitsVRAMQualityFit
Q2_K
2
1.5 GB
LowB56
Q3_K_S
3
1.9 GB
LowB56
NVFP4
4
2.1 GB
MediumB56
Q4_K_M
4
2.3 GB
MediumB56
Q5_K_M
5
2.7 GB
HighB56
Q6_K
6
3.1 GB
HighB56
Q8_0
8
4.1 GB
Very HighB56
F16Best for your GPU
16
7.8 GB
MaximumB56

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.

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.

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