Can Phi 3.5 Mini 4B run on RTX 4080 Super 16GB?

YES — Runs Great

A71Great

○Estimated from fit model

Phi 3.5 Mini 4B needs ~10.8 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 10.8 GB, 64.0 tok/s, Runs well

10.8 GB required16.0 GB available

68% VRAM used

Fit status

Runs well

Decode

64.0 tok/s

TTFT

3025 ms

Safe context

30K

Memory

10.8 GB / 16.0 GB

Memory breakdown

Weights2.4 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsPhi 3.5 Mini 4B on RTX 4080 Super 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: 64.0 tok/s decode · 3.0s TTFT (warm) · 160 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	64.0 tok/s	1650 ms	30K
Coding	A	Runs well	64.0 tok/s	3025 ms	30K
Agentic Coding	B	Runs with offload (needs ~0.1 GB host RAM)	64.0 tok/s	4400 ms	30K
Reasoning	A	Runs well	64.0 tok/s	3575 ms	30K
RAG	B	Runs with offload (needs ~0.1 GB host RAM)	64.0 tok/s	5500 ms	30K

Quantization options

How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	B62
Q3_K_S	3	2.0 GB	Low	B62
NVFP4	4	2.2 GB	Medium	B62
Q4_K_M	4	2.4 GB	Medium	B62
Q5_K_M	5	2.9 GB	High	B63
Q6_K	6	3.3 GB	High	B63
Q8_0	8	4.3 GB	Very High	B64
F16Best for your GPU	16	8.2 GB	Maximum	B67

Get started

Copy-paste commands to run Phi 3.5 Mini 4B on your machine.

Run

ollama run phi3.5

Your hardware

More models your RTX 4080 Super 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	115.5 tok/s
👁 Alibaba Qwen 3 14B	14B	S	88.4 tok/s
👁 Alibaba Qwen 3 8B	8B	S	128 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	75.5 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	63.6 tok/s

Frequently asked questions

See all results for RTX 4080 Super 16GB See all hardware for Phi 3.5 Mini 4B

URL: https://willitrunai.com/can-run/phi-3.5-mini-4b-on-rtx-4080-super-16gb

⇱ Phi 3.5 Mini 4B on RTX 4080 Super 16GB? YES