Can Phi 3.5 Mini 4B run on RTX 4080 Laptop 12GB?

YES — Tight Fit

B68Good

Estimated from fit model

Phi 3.5 Mini 4B needs ~10.4 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 10.4 GB, 64.0 tok/s, Tight fit

10.4 GB required12.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

64.0 tok/s

TTFT

3025 ms

Safe context

20K

Memory

10.4 GB / 12.0 GB

Memory breakdown

Weights2.4 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsPhi 3.5 Mini 4B on RTX 4080 Laptop 12GB
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	20K
Coding	B	Tight fit	64.0 tok/s	3025 ms	20K
Agentic Coding	F	Too heavy	50.3 tok/s	5601 ms	20K
Reasoning	B	Tight fit	64.0 tok/s	3575 ms	20K
RAG	F	Too heavy	50.3 tok/s	7001 ms	20K

Quantization options

How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	B63
Q3_K_S	3	2.0 GB	Low	B64
NVFP4	4	2.2 GB	Medium	B64
Q4_K_M	4	2.4 GB	Medium	B64
Q5_K_M	5	2.9 GB	High	B65
Q6_K	6	3.3 GB	High	B65
Q8_0	8	4.3 GB	Very High	B67
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

Opciones de mejora

Hardware que ejecuta bien Phi 3.5 Mini 4B

RTX 5060 Ti 16GBOpción económica

16 GB VRAM (+4)448 GB/s (+16)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.76 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$449 MSRP

RTX 4060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+4)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.64 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

RTX 5070 Ti 16GBMejora NVIDIA

16 GB VRAM (+4)896 GB/s (+464)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.76 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$749 MSRP

Frequently asked questions

Can RTX 4080 Laptop 12GB run Phi 3.5 Mini 4B?

Yes, RTX 4080 Laptop 12GB can run Phi 3.5 Mini 4B with a B grade (Tight fit). Expected decode speed: 64.0 tok/s.

How much VRAM does Phi 3.5 Mini 4B need?

Phi 3.5 Mini 4B (4B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3.5 Mini 4B?

The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3.5 Mini 4B run at on RTX 4080 Laptop 12GB?

On RTX 4080 Laptop 12GB, Phi 3.5 Mini 4B achieves approximately 64.0 tokens per second decode speed with a time-to-first-token of 3025ms using Q4_K_M quantization.

Can RTX 4080 Laptop 12GB run Phi 3.5 Mini 4B for coding?

For coding workloads, Phi 3.5 Mini 4B on RTX 4080 Laptop 12GB receives a B grade with 64.0 tok/s and 20K context.

What context window can Phi 3.5 Mini 4B use on RTX 4080 Laptop 12GB?

On RTX 4080 Laptop 12GB, Phi 3.5 Mini 4B can safely use up to 20K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 4080 Laptop 12GB See all hardware for Phi 3.5 Mini 4B

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