Can gemma 3 4b it run on RTX 5000 Ada Laptop 16GB?

YES — Runs Great

C48Usable

Estimated from fit model

gemma 3 4b it needs ~5.4 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~56 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) — 5.4 GB, 64.0 tok/s, Runs well

5.4 GB required16.0 GB available

34% VRAM used

Fit status

Runs well

Decode

64.0 tok/s

TTFT

3025 ms

Safe context

378K

Memory

5.4 GB / 16.0 GB

Memory breakdown

Weights2.4 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsgemma 3 4b it on RTX 5000 Ada Laptop 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	C	Runs well	64.0 tok/s	1650 ms	378K
Coding	C	Runs well	56.0 tok/s	3457 ms	378K
Agentic Coding	C	Runs well	64.0 tok/s	4400 ms	378K
Reasoning	C	Runs well	64.0 tok/s	3575 ms	378K
RAG	C	Runs well	64.0 tok/s	5500 ms	378K

Quantization options

How gemma 3 4b it (4B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	C46
Q3_K_S	3	2.0 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run gemma 3 4b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-4b-it-gguf && lms server start

Upgrade options

Hardware that runs gemma 3 4b it well

MacBook Pro M4 Pro 24GBBudget pick

24 GB Unified (+8)

This setup is broadly balanced for this model.56 tok/s decode

~$1,999 MSRP

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run gemma 3 4b it?

Yes, RTX 5000 Ada Laptop 16GB can run gemma 3 4b it with a C grade (Runs well). Expected decode speed: 56.0 tok/s.

How much VRAM does gemma 3 4b it need?

gemma 3 4b it (4B parameters) requires approximately 5.4 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 4b it?

The recommended quantization for gemma 3 4b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 4b it run at on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, gemma 3 4b it achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run gemma 3 4b it for coding?

For coding workloads, gemma 3 4b it on RTX 5000 Ada Laptop 16GB receives a C grade with 56.0 tok/s and 378K context.

What context window can gemma 3 4b it use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, gemma 3 4b it can safely use up to 378K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada Laptop 16GB See all hardware for gemma 3 4b it

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