Nov 1, 2024 · Our xMADified Gemma 2 (9B) model is here, quantized from 16-bit floats down to a lean, mean 4-bit machine. With our proprietary tech, the xMADified Gemma 2 brings you accuracy, memory efficiency, and fine-tuning ease at a fraction of the VRAM you’d expect.

Jul 23, 2024 · Gemma 2 is available in 9 billion (9B) and 27 billion (27B) parameter sizes; Gemma 2 is higher-performing and more efficient at inference than the first generation, with significant safety...

Jun 28, 2024 · Four months after Google introduced the Gemma 2 in Google IO 2024, they are finally making it available to researchers and developers worldwide. The tech giant is releasing the model in two variants — 9 billion and 27 billion parameters. But Google isn’t stopping with just two sizes of Gemma 2.

To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

Jun 27, 2024 · Outsized performance: At 27B, Gemma 2 delivers the best performance for its size class, and even offers competitive alternatives to models more than twice its size. The 9B Gemma 2 model also delivers class-leading performance, outperforming Llama 3 8B and other open models in its size category.

Jun 28, 2024 · Model Variants: Gemma 2 comes in two versions – 9 billion parameters (9B) and 27 billion parameters (27B). The 9B model outperforms Llama-3 8B in several benchmarks. The 27B model is...

Oct 23, 2024 · Here is the list of models you can run on single 24GB GPU (without CPU offloading) which works great as a local LLM solution.

Gemma is compatible across NVIDIA AI platforms—from the datacenter, cloud, to the local PC with RTX GPU systems. Gemma models use a vocabulary size of 256K and support a context length of up to 4K while using rotary positional embedding (RoPE).

Jun 27, 2024 · Google claims the 27B model can run at full precision on a single Google Cloud TPU host, NVIDIA A100 80GB Tensor Core GPU, or NVIDIA H100 Tensor Core GPU. THis significantly reduces the hardware requirements and costs associated with deploying such powerful AI models.

For Gemma-2-9B model, you would require approximately 40GB of hard disk space and 40GB of VRAM. As for RAM, 8GB or more should suffice since the computation is mainly handled by the GPU. Regarding additional costs, it totally depends on where you are running the model for inference, whether locally or on some cloud compute.