Can Phi 3 Mini 3.8B run on NVIDIA V100 32GB?

YES — Runs Great

B66Good

Estimated from fit model

Phi 3 Mini 3.8B needs ~12.6 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 12.6 GB, 53.2 tok/s, Runs well

12.6 GB required32.0 GB available

39% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

69K

Memory

12.6 GB / 32.0 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B on NVIDIA V100 32GB
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.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	53.2 tok/s	1985 ms	69K
Coding	B	Runs well	53.2 tok/s	3639 ms	69K
Agentic Coding	B	Runs well	53.2 tok/s	5293 ms	69K
Reasoning	B	Runs well	53.2 tok/s	4301 ms	69K
RAG	B	Runs well	53.2 tok/s	6617 ms	69K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B60
Q3_K_S	3	1.9 GB	Low	B60
NVFP4	4

Get started

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

Run

ollama run phi3:mini

Upgrade options

Hardware that runs Phi 3 Mini 3.8B well

MacBook Pro M3 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.53.2 tok/s decode

~$2,499 MSRP

MacBook Pro M4 Max 48GBBest value

48 GB Unified (+16)

This setup is broadly balanced for this model.53.2 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run Phi 3 Mini 3.8B?

Yes, NVIDIA V100 32GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 53.2 tok/s.

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 12.6 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Mini 3.8B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on NVIDIA V100 32GB receives a B grade with 53.2 tok/s and 69K context.

What context window can Phi 3 Mini 3.8B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Phi 3 Mini 3.8B can safely use up to 69K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Phi 3 Mini 3.8B

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