Can Phi 3 Mini 3.8B run on NVIDIA A2 16GB?

YES — Runs Great

A72Great

Estimated from fit model

Phi 3 Mini 3.8B needs ~11.0 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 11.0 GB, 53.2 tok/s, Runs well

11.0 GB required16.0 GB available

69% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

30K

Memory

11.0 GB / 16.0 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B on NVIDIA A2 16GB
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	30K
Coding	A	Runs well	53.2 tok/s	3639 ms	30K
Agentic Coding	B	Runs with offload (needs ~0.1 GB host RAM)	45.3 tok/s	6211 ms	30K
Reasoning	A	Runs well	53.2 tok/s	4301 ms	30K
RAG	B	Runs with offload (needs ~0.1 GB host RAM)	45.3 tok/s	7764 ms	30K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B63
Q3_K_S	3	1.9 GB	Low	B63
NVFP4	4	2.1 GB	Medium	B63
Q4_K_M	4	2.3 GB	Medium	B63
Q5_K_M	5	2.7 GB	High	B64
Q6_K	6	3.1 GB	High	B64
Q8_0	8	4.1 GB	Very High	B65
F16Best for your GPU	16	7.8 GB	Maximum	B69

Get started

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

Run

ollama run phi3:mini

Your hardware

More models your NVIDIA A2 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	30.5 tok/s
Qwen 3 14B	14B	S	19.7 tok/s
Qwen 3.5 4B	4B	S	56 tok/s
Qwen 3 8B	8B	S	34.4 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	18.7 tok/s

Frequently asked questions

Can NVIDIA A2 16GB run Phi 3 Mini 3.8B?

Yes, NVIDIA A2 16GB can run Phi 3 Mini 3.8B with a A 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 11.0 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 A2 16GB?

On NVIDIA A2 16GB, 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 A2 16GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on NVIDIA A2 16GB receives a A grade with 53.2 tok/s and 30K context.

What context window can Phi 3 Mini 3.8B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Phi 3 Mini 3.8B can safely use up to 30K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for Phi 3 Mini 3.8B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

53.2 tok/s

1985 ms

30K

Coding

Runs well

53.2 tok/s

3639 ms

30K

Agentic Coding

Runs with offload (needs ~0.1 GB host RAM)

45.3 tok/s

6211 ms

30K

Reasoning

Runs well

53.2 tok/s

4301 ms

30K

RAG

Runs with offload (needs ~0.1 GB host RAM)

45.3 tok/s

7764 ms

30K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B63
Q3_K_S	3	1.9 GB	Low	B63
NVFP4	4	2.1 GB	Medium	B63
Q4_K_M	4	2.3 GB	Medium	B63
Q5_K_M	5	2.7 GB	High	B64
Q6_K	6	3.1 GB	High	B64
Q8_0	8	4.1 GB	Very High	B65
F16Best for your GPU	16	7.8 GB	Maximum	B69

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

30.5 tok/s

Qwen 3 14B

14B

19.7 tok/s

Qwen 3.5 4B

56 tok/s

Qwen 3 8B

34.4 tok/s

Phi-4-reasoning-plus 14B

14.7B

18.7 tok/s

Frequently asked questions

Can NVIDIA A2 16GB run Phi 3 Mini 3.8B?

Yes, NVIDIA A2 16GB can run Phi 3 Mini 3.8B with a A 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 11.0 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 A2 16GB?

On NVIDIA A2 16GB, 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 A2 16GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on NVIDIA A2 16GB receives a A grade with 53.2 tok/s and 30K context.

What context window can Phi 3 Mini 3.8B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Phi 3 Mini 3.8B can safely use up to 30K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.