Thurmon Demich

Posted on Jul 14 • Originally published at bestgpuforllm.com

Best GPU for Gemma 2B-27B in 2026 (6 Picks Ranked)

#gpu #gemma #google #llm

This article was originally published on Best GPU for LLM. The full version with interactive tools, FAQ, and live pricing is on the original site.

Which GPU do you actually need for Gemma? That depends entirely on the model size. Gemma 2B runs on practically anything. Gemma 7B needs 8-12GB of VRAM. Gemma 27B demands 24GB. Here is exactly what to buy for each variant.

Who this is for

You want to run Google's Gemma models locally -- maybe for privacy, offline access, or to avoid API costs. Gemma is popular because Google optimized it for efficiency, and the smaller variants punch above their weight. This guide matches each Gemma size to the right GPU.

Gemma models and VRAM requirements

Model	Parameters	Q4_K_M Size	Minimum VRAM	Notes
Gemma 2B	2B	~1.5GB	6GB	Runs on almost anything
Gemma 7B	7B	~4.5GB	8GB	Best balance of size/quality
Gemma 2 9B	9B	~5.5GB	10GB	Improved architecture
Gemma 2 27B	27B	~16GB	24GB	Needs headroom for context

Gemma 7B and Gemma 2 9B are the most popular choices for local deployment. Both fit comfortably on 16GB cards with room for long context windows.

VRAM chart available at the original article

GPU speed benchmarks for Gemma

Tested with Ollama, Q4_K_M quantization:

GPU	Gemma 7B	Gemma 2 9B	Gemma 2 27B	Price
RTX 5090 (32GB)	~95 tok/s	~80 tok/s	~30 tok/s	~$2,000
RTX 4090 (24GB)	~65 tok/s	~55 tok/s	~22 tok/s	~$1,600
RTX 5080 (16GB)	~55 tok/s	~48 tok/s	Won't fit	~$1,000
RTX 4070 Ti Super (16GB)	~40 tok/s	~35 tok/s	Won't fit	~$700
RTX 4060 Ti 16GB	~35 tok/s	~30 tok/s	Won't fit	~$400
RTX 3060 12GB (used)	~25 tok/s	~20 tok/s	Won't fit	~$250

Gemma 7B at 35 tok/s on an RTX 4060 Ti 16GB feels snappy for interactive chat. You do not need a flagship card unless you are running the 27B variant.

Which GPU should you buy for Gemma?

If you want Gemma 7B or 9B for general chat and tasks, the RTX 4060 Ti 16GB ($400) is the sweet spot -- plenty of VRAM and fast enough for real-time conversation. If you want Gemma 2 27B for the highest quality local Gemma experience, you need 24GB -- the RTX 4090 ($1,600) or a used RTX 3090 ($900) are your options. If you are on a tight budget and only care about Gemma 7B, a used RTX 3060 12GB ($250) gets the job done at 25 tok/s.

Common mistakes to avoid

Buying a 24GB card just for Gemma 7B. The model only needs ~6GB at Q4_K_M. A $400 RTX 4060 Ti 16GB handles it with 10GB to spare. Save the money.
Ignoring Gemma 2 9B. It outperforms the original Gemma 7B on most benchmarks with only slightly higher VRAM usage. If your GPU fits 7B, it almost certainly fits 9B too.
Running Gemma 27B at Q2 quantization to fit it on 16GB. The quality degradation at Q2 is severe. Either get a 24GB card or stick with the 9B model, which will produce better results than a heavily quantized 27B.
Choosing Gemma 2B when 7B fits your hardware. The 2B model is significantly weaker. Unless you are running on a laptop with integrated graphics, jump to 7B.

Our recommendation

Your goal	Best GPU	Price
Gemma 7B/9B daily driver	RTX 4060 Ti 16GB	~$400
Gemma 27B local	RTX 4090	~$1,600
Budget Gemma setup	RTX 3060 12GB (used)	~$250
Maximum Gemma speed	RTX 5090	~$2,000

Gemma models are efficient enough that you do not need to overspend on hardware. The RTX 4060 Ti 16GB handles the two most popular variants at comfortable speeds, and at $400 it is one of the best value propositions in local LLM hardware.

Gemma is one of the few model families where a $400 GPU gives you a genuinely good experience. Do not overthink this purchase.

If you plan to run Gemma through Ollama, check our Ollama GPU guide for setup tips. Running the latest Gemma generation? See our Gemma 3 GPU guide for the updated VRAM requirements, or jump straight to the newest release with our Gemma 4 GPU guide. For a broader look at VRAM planning across model families, see our VRAM requirements guide.

Frequently Asked Questions

How much VRAM does Gemma 27B need?

Gemma 2 27B requires approximately 16GB VRAM at Q4_K_M quantization, but you need a 24GB card for comfortable use because the KV cache and context window add 4-8GB on top. The RTX 4090 (24GB) or a used RTX 3090 (24GB) are the minimum recommended GPUs. A 16GB card cannot fit Gemma 27B at any usable quantization level. For the latest generation, see how much VRAM Gemma 4 needs.

What is Gemma 27B's inference speed on an RTX 4090?

Gemma 2 27B runs at roughly 20-25 tokens per second on an RTX 4090 at Q4_K_M quantization with Ollama — fast enough for comfortable interactive chat. The RTX 5090 pushes this into the 25-35 tok/s range. Smaller models like Gemma 7B are significantly faster, typically delivering conversational speeds well above 50 tok/s on the same card.

Can I run Gemma 27B on 16GB VRAM?

No, not practically. Gemma 2 27B at Q4_K_M is approximately 16GB for the model weights alone, leaving zero room for the KV cache and context window. You would need to use Q2_K quantization which severely degrades output quality. A 24GB GPU like the RTX 4090 or used RTX 3090 is the minimum for usable Gemma 27B inference.

Gemma 2B vs 7B vs 27B — which should I run?

Run the largest variant your GPU can handle comfortably. Gemma 2B is only suitable for very constrained hardware or embedding tasks — its output quality is noticeably weaker. Gemma 7B and 9B are the sweet spot for most users, fitting on 8-16GB cards with good performance. Gemma 27B produces the highest quality output but requires 24GB VRAM, making it practical only on RTX 4090 or RTX 3090 class hardware.

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