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.
NVIDIA GeForce RTX 4060 Ti 16GB
16GB GDDR6Handles Gemma 7B and 9B at 35 tok/s with 16GB VRAM — best bang-for-buck for the most popular Gemma variants.
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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 | 5.5GB | 8GB | Best balance of size/quality |
| Gemma 2 9B | 9B | 5.8GB | 10GB | Improved architecture |
| Gemma 2 27B | 27B | 17GB | 24GB | Needs headroom for context |
Those sizes are Ollama’s published download sizes, not estimates — gemma2:9b is 5.4GB and gemma2:27b is 16GB, with the explicit q4_K_M builds a few hundred megabytes larger. Check them before buying; Google reissues these and the sizes move.
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 capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
GPU speed benchmarks for Gemma
Ollama at Q4_K_M quantization. These are bandwidth-derived estimates, not lab results — see methodology:
| GPU | Gemma 7B | Gemma 2 9B | Gemma 2 27B | Price |
|---|---|---|---|---|
| RTX 5090 (32GB) | ~95 tok/s | ~80 tok/s | ~30 tok/s | ~$4,900 |
| RTX 4090 (24GB) | ~65 tok/s | ~55 tok/s | ~22 tok/s | ~$2,200 |
| RTX 5080 (16GB) | ~55 tok/s | ~48 tok/s | Won’t fit | ~$1,400 |
| RTX 4070 Ti Super (16GB) | ~40 tok/s | ~35 tok/s | Won’t fit | ~$800 |
| RTX 4060 Ti 16GB | ~35 tok/s | ~30 tok/s | Won’t fit | ~$425 |
| 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.
Check NVIDIA GeForce RTX 4060 Ti 16GB on Amazon→Buy on Shopee SG→Which GPU should you buy for Gemma?
If you want Gemma 7B or 9B for general chat and tasks, the RTX 4060 Ti 16GB ($425) 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 ($2,200) or a used RTX 3090 ($820) 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 is a 5.5GB download at Q4_K_M, as the table above says. A ~$425 RTX 4060 Ti 16GB handles it with over 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 | ~$425 |
| Gemma 27B local | RTX 4090 | ~$2,200 |
| Budget Gemma setup | RTX 3060 12GB (used) | ~$250 |
| Maximum Gemma speed | RTX 5090 | ~$4,900 |
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 ~$425 it is one of the best value propositions in local LLM hardware.
Check NVIDIA GeForce RTX 4060 Ti 16GB on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→NVIDIA GeForce RTX 3060 12GB
12GB GDDR6Cheapest way to run Gemma 7B locally — 12GB VRAM at $250 used, 25 tok/s is enough for daily chat.
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Gemma is one of the few model families where a $425 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.