Can Gemma 2 2B run on RTX 3050 Ti Laptop 4GB?

Q: What should I upgrade first if Gemma 2 2B feels slow on RTX 3050 Ti Laptop 4GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C54Usable

Estimated from fit model

Gemma 2 2B needs ~4.1 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: 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) — 4.1 GB, 24.0 tok/s, Runs with offload (needs ~0 GB host RAM)

4.1 GB required4.0 GB available

102% VRAM needed

100 MB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0 GB host RAM)

Decode

24.0 tok/s

TTFT

8067 ms

Safe context

Memory

4.1 GB / 4.0 GB

Memory breakdown

Weights1.2 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

See how fast it feelsGemma 2 2B on RTX 3050 Ti Laptop 4GB
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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	24.0 tok/s	4400 ms	8K
Coding	C	Runs with offload (needs ~0 GB host RAM)	24.0 tok/s	8067 ms	8K
Agentic Coding	F	Too heavy	24.0 tok/s	11733 ms	8K
Reasoning	C	Runs with offload (needs ~0 GB host RAM)	24.0 tok/s	9533 ms	8K
RAG	F	Too heavy	24.0 tok/s	14667 ms	8K

Quantization options

How Gemma 2 2B (2B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	B61
Q3_K_S	3	1.0 GB	Low	B60
NVFP4	4

Get started

Copy-paste commands to run Gemma 2 2B on your machine.

Run

lms load gemma-2-2b-it && lms server start

Upgrade options

Hardware that runs Gemma 2 2B well

GTX 1660 Super 6GBBudget pick

6 GB VRAM (+2)336 GB/s (+144)

Adds memory headroom for longer context windows and future model growth.28 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$229 MSRP

GTX 1060 6GBBest value

6 GB VRAM (+2)

Adds memory headroom for longer context windows and future model growth.28 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$249 MSRP

RTX 3050 8GBNVIDIA upgrade

8 GB VRAM (+4)224 GB/s (+32)

Adds memory headroom for longer context windows and future model growth.24 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$249 MSRP

Frequently asked questions

Can RTX 3050 Ti Laptop 4GB run Gemma 2 2B?

Yes, RTX 3050 Ti Laptop 4GB can run Gemma 2 2B with a C grade (Runs with offload (needs ~0 GB host RAM)). Expected decode speed: 24.0 tok/s.

How much VRAM does Gemma 2 2B need?

Gemma 2 2B (2B parameters) requires approximately 4.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 2B?

The recommended quantization for Gemma 2 2B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 2B run at on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Gemma 2 2B achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8067ms using Q4_K_M quantization.

Can RTX 3050 Ti Laptop 4GB run Gemma 2 2B for coding?

For coding workloads, Gemma 2 2B on RTX 3050 Ti Laptop 4GB receives a C grade with 24.0 tok/s and 8K context.

What context window can Gemma 2 2B use on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Gemma 2 2B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 2 2B feels slow on RTX 3050 Ti Laptop 4GB?

See all results for RTX 3050 Ti Laptop 4GB See all hardware for Gemma 2 2B

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