What is the context window of Google: Gemma 3 12B?

Google: Gemma 3 12B accepts up to 131,072 tokens (131K) of context per request.

How much does Google: Gemma 3 12B cost per million tokens?

Through Wide Area Intelligence, Google: Gemma 3 12B costs $0.055 per 1M input tokens and $0.17 per 1M output tokens when served from the cloud, billed from prepaid credits. A workload of 1M input plus 1M output tokens costs $0.22.

Can I run Google: Gemma 3 12B on my own hardware?

Yes — Google: Gemma 3 12B is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 8.5 GB of GPU memory with an 8,192-token context — it fits a single 12 GB card. Wide Area Intelligence routes requests to your own nodes first and fails over to the cloud only when they can't serve.

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Google: Gemma 3 12B

Name: Google: Gemma 3 12B
Brand: google
Price: 0.0550 USD

google/gemma-3-12b-it

↓ runs free on your own hardware⚙ tool calling

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...

specs & pricing

Type: text
Provider: google
Model ID: google/gemma-3-12b-it
Capabilities: vision, tools
Context window: 131K tokens
Self-hostable: Yes — runs on your own GPU
Input price: $0.055 / 1M tokens
Output price: $0.17 / 1M tokens

Cloud price is billed from prepaid credits when a request fails over to the cloud. Open-weight models run free on GPUs you own — the gateway routes to your nodes first.

What it costs to run

cloud · 1M in + 1M out

$0.22

$0.055 for a million input tokens plus $0.17 for a million output tokens, billed from credits only when a request fails over to the cloud.

your hardware

$0 / token

Served from a workstation or on-prem server you own, there is no per-token fee — the marginal cost is power.

AI cost calculator →GPU power cost →

Run it on your own hardware

Estimated GPU memory for Google: Gemma 3 12B (12B parameters), including the KV cache for a 8,192-token context.

Q4_K_M: ~8.5 GBfits a 12 GB card
Q8_0: ~13.6 GBfits a 16 GB card
FP16 / BF16: ~24.3 GBfits a 32 GB card

Install the agent on the machine and it dials out to the gateway over a single WebSocket — no port forwarding or inbound firewall rules. Requests for this model go to your node first and fail over to the cloud only when it can't serve.

VRAM calculator →Which GPU do I need? →

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Call Google: Gemma 3 12B through one OpenAI-compatible endpoint.

Serve it from hardware you own, with cloud failover when your nodes are busy or offline.

Run it on your own hardware Try it in chat

no credit card · 2 nodes free · openai-compatible

Frequently asked questions

What is the context window of Google: Gemma 3 12B?: Google: Gemma 3 12B accepts up to 131,072 tokens (131K) of context per request.
How much does Google: Gemma 3 12B cost per million tokens?: Through Wide Area Intelligence, Google: Gemma 3 12B costs $0.055 per 1M input tokens and $0.17 per 1M output tokens when served from the cloud, billed from prepaid credits. A workload of 1M input plus 1M output tokens costs $0.22.
Can I run Google: Gemma 3 12B on my own hardware?: Yes — Google: Gemma 3 12B is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 8.5 GB of GPU memory with an 8,192-token context — it fits a single 12 GB card. Wide Area Intelligence routes requests to your own nodes first and fails over to the cloud only when they can't serve.

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