How much VRAM does Gemma 3 12B need?

Gemma 3 12B requires 10.5 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 5.7 GB.

What is the best GPU for Gemma 3 12B?

The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Gemma 3 12B, achieving 250 tok/s at Q8_0 with an excellent rating.

Can I run Gemma 3 12B on an RTX 4060 Ti?

Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Gemma 3 12B runs at 42 tok/s (Q5_K_M, good).

What quantization should I use for Gemma 3 12B?

For the best quality, use Q6_K (10.5 GB VRAM). If your GPU has limited VRAM, Q3_K_M (5.7 GB) is the most efficient option with acceptable quality.

Is Gemma 3 12B good for coding?

Yes. Gemma 3 12B is used with Cursor, Continue, Aider, Open WebUI, LM Studio for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.

ChatCodingMulti-purpose12.2B

Chat

Gemma 3 12B

Gemma · Gemma license

Google's efficient 12B model with chat and coding support.

Parameters: 12.2B
Architecture: Dense
Context: 32,768 tokens
Released: 2025-03-12
Engines: llama.cpp, ollama, vLLM, TGI
Builder Tools: Cursor, Continue, Aider, Open WebUI, LM Studio

Parameters

12.2B

VRAM

10.5 GB

Context

32K

Formats

GPUs

Gemma 3 12B (12.2B) requires 10.5 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 7 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 250 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.

Source: OwnRig methodology

VRAM (Recommended)

10.5 GB

Quantization

Q6_K

File Size

9.2 GB

Max Context

32K tokens

Primary Use

Chat

Memory

VRAM Requirements

Quality	Quantization	VRAM	File Size
full	Q8_0	13.5 GB	12 GB
recommended	Q6_K	10.5 GB	9.2 GB
recommended	Q5_K_M	8.8 GB	7.5 GB
efficient	Q4_K_M	7 GB	6 GB
compressed	Q3_K_M	5.7 GB	4.8 GB

Scaling

Context Length Impact

KV cache VRAM at Q6_K quality. Longer context = more memory.

Context	KV Cache	Total VRAM
2K	205 MB	10.7 GB
4K	410 MB	10.9 GB
8K	819 MB	11.3 GB
16K	1.5 GB	12 GB
32K	3.1 GB	13.6 GB

Compatible GPUs

23 devices


NVIDIA Grace Blackwell Ultra GB300	Q8_0	250 tok/s	Excellent
NVIDIA GeForce RTX 4090	Q5_K_M	75 tok/s	Excellent
NVIDIA GeForce RTX 5080	Q5_K_M	72 tok/s	Excellent
AMD Radeon Pro W7900	Q5_K_M	81 tok/s	Excellent
NVIDIA GeForce RTX 3060 12GB	Q4_K_M	32 tok/s	Good
NVIDIA GeForce RTX 4060 Ti 16GB	Q5_K_M	42 tok/s	Good
NVIDIA GeForce RTX 4070 Ti 12GB	Q4_K_M	32 tok/s	Good
NVIDIA RTX 4090 Laptop (150-175W)	Q5_K_M	36 tok/s	Good
AMD Radeon RX 7900 XTX	Q5_K_M	64 tok/s	Good
NVIDIA RTX PRO 6000 Blackwell	Q8_0	80 tok/s	Good
NVIDIA RTX PRO 6000 Blackwell Max-Q	Q8_0	74 tok/s	Good
AMD Radeon RX 9070	Q5_K_M	74 tok/s	Good
NVIDIA GeForce RTX 5060 Ti 16GB	Q5_K_M	47 tok/s	Good
NVIDIA GeForce RTX 3080 10GB	Q3_K_M	28 tok/s	Acceptable
NVIDIA RTX 4060 Laptop (40-60W)	Q3_K_M	11 tok/s	Acceptable
NVIDIA RTX 4070 Laptop (80-115W)	Q3_K_M	13 tok/s	Acceptable
NVIDIA RTX 4080 Laptop (120-150W)	Q4_K_M	22 tok/s	Acceptable
AMD Radeon RX 9060 XT 16GB	Q5_K_M	37 tok/s	Acceptable
Apple M3 Pro (18GB Unified)	Q3_K_M	5 tok/s	Marginal
NVIDIA GeForce RTX 4060 8GB	Q3_K_M	18 tok/s	Marginal
AMD Radeon RX 7600	Q3_K_M	14 tok/s	Marginal
NVIDIA GeForce RTX 5060 8GB	Q3_K_M	21 tok/s	Marginal
AMD Radeon RX 9060 XT 8GB	Q5_K_M	–	Not viable

Showing 23 of 23 entries

Builder Context

Gemma 3 12B is commonly used with Cursor, Continue, Aider, Open WebUI, LM Studio. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.

Hardware

Recommended Builds

Complete PC builds that can run Gemma 3 12B.

Budget

Budget Home AI Server

Always-on AI assistant for the whole household

RTX 4060 Ti 16GB·16 GBVRAM

Runs 7 models

$1,063

FAQ

Frequently Asked Questions

How much VRAM does Gemma 3 12B need?: Gemma 3 12B requires 10.5 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 5.7 GB.
What is the best GPU for Gemma 3 12B?: The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Gemma 3 12B, achieving 250 tok/s at Q8_0 with an excellent rating.
Can I run Gemma 3 12B on an RTX 4060 Ti?: Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Gemma 3 12B runs at 42 tok/s (Q5_K_M, good).
What quantization should I use for Gemma 3 12B?: For the best quality, use Q6_K (10.5 GB VRAM). If your GPU has limited VRAM, Q3_K_M (5.7 GB) is the most efficient option with acceptable quality.
Is Gemma 3 12B good for coding?: Yes. Gemma 3 12B is used with Cursor, Continue, Aider, Open WebUI, LM Studio for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.

Related Guides

Tutorial

The Complete Guide to Running LLMs Locally

Run large language models locally: hardware needs, Ollama and llama.cpp, model picks by use case, and quantization.

All models

Data confidence: estimated. Source

VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.Gemma is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.