AllocatePearl-ai Gemma-4-31B-it-pearl GPU requirements
What it takes to run Pearl-ai Gemma-4-31B-it-pearl locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Pearl-ai Gemma-4-31B-it-pearl serves up to 262,144 tokens of context; the KV cache grows linearly toward that ceiling, so the slider below shows exactly what longer context costs in memory.
Smallest single device that fits: RTX 4090 (24 GB)
How it works
Common questions
How much VRAM does Pearl-ai Gemma-4-31B-it-pearl need?
At 8K context: roughly 70 GB at FP16, 35 GB at 8-bit, and 19 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Pearl-ai Gemma-4-31B-it-pearl?
At 4-bit, yes: a RTX 4090 (24 GB) or larger handles it at 8K context. At FP16 you need a A100 (80 GB) or larger.
Does quantization hurt Pearl-ai Gemma-4-31B-it-pearl's quality?
Modern 4-bit quantization costs a small amount of quality for a 4x memory saving; 8-bit is near-lossless. Validate on your own tasks before production.
What license is Pearl-ai Gemma-4-31B-it-pearl under?
The catalog does not list a license for this model. Check the lab’s model card for the exact terms before commercial deployment.
Is there an alternative to buying GPUs for Pearl-ai Gemma-4-31B-it-pearl?
Yes: managed inference. Allocate serves Pearl-ai Gemma-4-31B-it-pearl token-metered from $0.28 per million input tokens at provider list price, plus the 7% transaction fee. Idle time costs nothing.
More free tools
Allocate serves open-weight models like Pearl-ai Gemma-4-31B-it-pearl token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.