Will It Run AI

Can gemma 3 12b it run on RTX 3080 10GB?

YES — With Offload

C42Usable
Estimated from fit model

gemma 3 12b it needs ~10.6 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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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) — 10.6 GB, 51.0 tok/s, Runs with offload (needs ~0.4 GB host RAM)
10.6 GB required10.0 GB available
106% VRAM needed

0.6 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.4 GB host RAM)

Decode

51.0 tok/s

TTFT

3793 ms

Safe context

9K

Memory

10.6 GB / 10.0 GB

Offload

10%

Memory breakdown

Weights7.3 GB
KV Cache1.4 GB
Runtime0.9 GB
Headroom1.0 GB

See how fast it feels

See how fast it feelsgemma 3 12b it on RTX 3080 10GB
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: 51.0 tok/s decode · 3.8s TTFT (warm) · 128 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload77.3 tok/s1366 ms9K
CodingCRuns with offload (needs ~0.4 GB host RAM)51.0 tok/s3793 ms9K
Agentic CodingFToo heavy39.3 tok/s7168 ms9K
ReasoningCRuns with offload (needs ~0.4 GB host RAM)51.0 tok/s4483 ms9K
RAGFToo heavy39.3 tok/s8960 ms9K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.7 GB
LowC53
Q3_K_S
3
5.9 GB
LowC53
NVFP4Best for your GPU
4
6.7 GB
MediumC52
Q4_K_M
4
7.3 GB
MediumF0
Q5_K_M
5
8.6 GB
HighF0
Q6_K
6
9.8 GB
HighF0
Q8_0
8
12.8 GB
Very HighF0
F16
16
24.6 GB
MaximumF0

Get started

Copy-paste commands to run gemma 3 12b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien gemma 3 12b it

NVIDIARTX 3060 12GBOpción económica
12 GB VRAM (+2)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.31.8 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$329 MSRP

NVIDIARTX 5060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+6)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.36.8 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

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

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejora NVIDIA
16 GB VRAM (+6)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.30.2 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

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

~$499 MSRP

Frequently asked questions

Can RTX 3080 10GB run gemma 3 12b it?

Yes, RTX 3080 10GB can run gemma 3 12b it with a C grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 51.0 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 12b it?

The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 12b it run at on RTX 3080 10GB?

On RTX 3080 10GB, gemma 3 12b it achieves approximately 51.0 tokens per second decode speed with a time-to-first-token of 3793ms using Q4_K_M quantization.

Can RTX 3080 10GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on RTX 3080 10GB receives a C grade with 51.0 tok/s and 9K context.

What context window can gemma 3 12b it use on RTX 3080 10GB?

On RTX 3080 10GB, gemma 3 12b it can safely use up to 9K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if gemma 3 12b it feels slow on RTX 3080 10GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 3080 10GBSee all hardware for gemma 3 12b it
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