Gemma 27B vs Llama 70B?

Llama 70B is significantly better but needs 40GB+ VRAM. Gemma 27B is the best you can run on a 16GB card like RTX 4070 Ti Super.

Which has better coding performance?

Llama 3 is better for coding at all sizes. Gemma is more balanced toward general reasoning.

Can I run Gemma on a laptop?

Gemma 2 9B runs well on gaming laptops with 8GB+ VRAM. Gemma 2 2B can even run on integrated graphics.

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Model ComparisonUpdated December 2025

Gemma vs Llama

Google vs Meta open-weight models

Quick VerdictTie

At same parameter counts, Llama edges out. But Gemma 27B punches above its weight and fits in 16GB VRAM.

Choose Gemma 2 27B if:

Choose Gemma 27B if you have 16-24GB VRAM and want quality exceeding Llama 8B.

Choose Llama 3.1 8B if:

Choose Llama if you need long context, or want to scale up to 70B/405B later.

Gemma 2 from Google offers competitive performance in smaller packages. Here's how it compares to Meta's Llama 3.

Specifications

Specification	Gemma 2 27B	Llama 3.1 8B
Developer	Google	Meta
Parameters	27B	8B
Context Length	8K	128K
VRAM (Minimum)	16GB (Q4)	8GB (Q4)
VRAM (Recommended)	24GB	12GB
Release Date	June 2024	July 2024
License	Gemma Terms of Use	Llama 3.1 Community License

Benchmark Comparison

Category	Gemma 2 27B	Llama 3.1 8B	Winner
MMLU (27B vs 8B)	75.2%	69.4%	Gemma 2 27B
Coding	68.0%	72.6%	Llama 3.1 8B
Context Length	8K	128K	Llama 3.1 8B
VRAM (Q4)	~15GB	~6GB	Llama 3.1 8B
Size/Quality Ratio	Excellent	Good	Gemma 2 27B

Gemma 2 27B

by Google

Strengths

Efficient size
Google's training expertise
Good at reasoning
Runs on consumer GPUs

Weaknesses

Smaller context window
Fewer size options
Less community momentum

Best For

Consumer GPU usersWhen 27B is enoughEfficient inference

How to Run Gemma 2 27B Locally →

Llama 3.1 8B

by Meta

Strengths

Massive context
Huge community
Best documentation
More size options up to 405B

Weaknesses

8B quality lower than Gemma 27B
Need bigger models for Gemma parity

Best For

Long context tasksWhen you need the Llama ecosystem

How to Run Llama 3.1 8B Locally →

Frequently Asked Questions

Related Comparisons

Llama vs Mistral

Phi vs Llama

Need Hardware for These Models?

Check our GPU buying guides to find the right hardware for running LLMs locally.

Loading AI models database...

Model ComparisonUpdated December 2025

Gemma vs Llama

Google vs Meta open-weight models

Quick VerdictTie

At same parameter counts, Llama edges out. But Gemma 27B punches above its weight and fits in 16GB VRAM.

Choose Gemma 2 27B if:

Choose Gemma 27B if you have 16-24GB VRAM and want quality exceeding Llama 8B.

Choose Llama 3.1 8B if:

Choose Llama if you need long context, or want to scale up to 70B/405B later.

Gemma 2 from Google offers competitive performance in smaller packages. Here's how it compares to Meta's Llama 3.

Specifications

Specification	Gemma 2 27B	Llama 3.1 8B
Developer	Google	Meta
Parameters	27B	8B
Context Length	8K	128K
VRAM (Minimum)	16GB (Q4)	8GB (Q4)
VRAM (Recommended)	24GB	12GB
Release Date	June 2024	July 2024
License	Gemma Terms of Use	Llama 3.1 Community License

Benchmark Comparison

Category	Gemma 2 27B	Llama 3.1 8B	Winner
MMLU (27B vs 8B)	75.2%	69.4%	Gemma 2 27B
Coding	68.0%	72.6%	Llama 3.1 8B
Context Length	8K	128K	Llama 3.1 8B
VRAM (Q4)	~15GB	~6GB	Llama 3.1 8B
Size/Quality Ratio	Excellent	Good	Gemma 2 27B

Gemma 2 27B

by Google

Strengths

Efficient size
Google's training expertise
Good at reasoning
Runs on consumer GPUs

Weaknesses

Smaller context window
Fewer size options
Less community momentum

Best For

Consumer GPU usersWhen 27B is enoughEfficient inference

How to Run Gemma 2 27B Locally →

Llama 3.1 8B

by Meta

Strengths

Massive context
Huge community
Best documentation
More size options up to 405B

Weaknesses

8B quality lower than Gemma 27B
Need bigger models for Gemma parity

Best For

Long context tasksWhen you need the Llama ecosystem

How to Run Llama 3.1 8B Locally →

Frequently Asked Questions

Related Comparisons

Llama vs Mistral

Phi vs Llama

Need Hardware for These Models?

Check our GPU buying guides to find the right hardware for running LLMs locally.