Can gemma 3 4b it run on RTX 5090 32GB?

YES — Runs Great

C45Usable

Estimated from fit model

gemma 3 4b it needs ~7.0 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 7.0 GB, 76.0 tok/s, Runs well

7.0 GB required32.0 GB available

22% VRAM used

Fit status

Runs well

Decode

76.0 tok/s

TTFT

2547 ms

Safe context

869K

Memory

7.0 GB / 32.0 GB

Memory breakdown

Weights2.4 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsgemma 3 4b it on RTX 5090 32GB
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: 76.0 tok/s decode · 2.5s TTFT (warm) · 190 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	76.0 tok/s	1389 ms	869K
Coding	C	Runs well	56.0 tok/s	3457 ms	869K
Agentic Coding	C	Runs well	76.0 tok/s	3705 ms	869K
Reasoning	C	Runs well	76.0 tok/s	3011 ms	869K
RAG	C	Runs well	76.0 tok/s	4632 ms	869K

Quantization options

How gemma 3 4b it (4B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	C43
Q3_K_S	3	2.0 GB	Low	C43
NVFP4	4	2.2 GB	Medium	C43
Q4_K_M	4	2.4 GB	Medium	C43
Q5_K_M	5	2.9 GB	High	C43
Q6_K	6	3.3 GB	High	C43
Q8_0	8	4.3 GB	Very High	C44
F16Best for your GPU	16	8.2 GB	Maximum	C45

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

Frequently asked questions

Can RTX 5090 32GB run gemma 3 4b it?

Yes, RTX 5090 32GB 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 7.0 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 5090 32GB?

On RTX 5090 32GB, 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 5090 32GB run gemma 3 4b it for coding?

For coding workloads, gemma 3 4b it on RTX 5090 32GB receives a C grade with 56.0 tok/s and 869K context.

What context window can gemma 3 4b it use on RTX 5090 32GB?

On RTX 5090 32GB, gemma 3 4b it can safely use up to 869K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5090 32GB See all hardware for gemma 3 4b it

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