NVIDIA joins hands with Google to launch Gemma, an optimized set of open models derived from Gemini technology. Powered by TensorRT-LLM, Gemma offers high throughput and cutting-edge performance. It’s compatible across NVIDIA AI platforms, making it accessible from data centers to local PCs.
Developed by Google DeepMind, Gemma 2B and Gemma 7B models prioritize efficiency. Accelerated by TensorRT-LLM, Gemma ensures seamless deployment and optimization. TensorRT-LLM’s simplified Python API streamlines quantization and kernel compression, enhancing customization options for Python developers.
Gemma models, boasting a vocabulary size of 256K and supporting context lengths of up to 8K, prioritize safety. Extensive data curation and PII filtering ensure responsible AI practices. Trained on over six trillion tokens, Gemma enables developers to build and deploy advanced AI applications confidently.
Accelerating Gemma Models with TensorRT-LLM
TensorRT-LLM plays a pivotal role in enhancing the speed of Gemma models. With its array of optimizations and kernels, TensorRT-LLM significantly improves inference throughput and latency. Notably, three distinct features—FP8, XQA, and INT4 Activation-aware weight quantization (INT4 AWQ)—contribute to boosting Gemma’s performance.
FP8 Enhancement:
FP8 represents a natural evolution in deep learning applications, surpassing the common 16-bit formats in modern processors. It facilitates higher throughput of matrix multiplication and memory transfers without compromising accuracy. FP8 benefits both small and large batch sizes, particularly in memory bandwidth-limited models.
FP8 Quantization for KV Cache:
TensorRT-LLM introduces FP8 quantization for the KV cache, addressing the unique challenges posed by large batch sizes or long context lengths. This optimization enables running batch sizes 2-3 times larger, thereby enhancing performance.
XQA Kernel:
The XQA kernel supports group query attention and multi-query attention, providing optimizations during generation phases and beam search. NVIDIA GPUs optimize data loading and conversion times, ensuring increased throughput within the same latency budget.
INT4 AWQ:
INT4 AWQ delivers superior performance with small batch size workloads, reducing a network’s memory footprint and significantly enhancing performance in memory bandwidth-limited applications. It leverages a low-bit weight-only quantization method to minimize quantization errors and protect salient weights.
Real-Time Performance with TensorRT-LLM
TensorRT-LLM, coupled with NVIDIA H200 Tensor Core GPUs, demonstrates remarkable real-time performance on Gemma 2B and Gemma 7B models. A single H200 GPU achieves over 79,000 tokens per second on the Gemma 2B model and nearly 19,000 tokens per second on the larger Gemma 7B model.
Enabling Scalability with TensorRT-LLM
Deploying the Gemma 2B model with TensorRT-LLM on just one H200 GPU allows serving over 3,000 concurrent users, all with real-time latency. This scalability underscores the efficiency and effectiveness of TensorRT-LLM in delivering high-performance AI solutions.
Getting Started with Gemma
Experience Gemma directly from the browser on the NVIDIA AI playground. Soon, you’ll also be able to try Gemma on the NVIDIA Chat with RTX demo app.
NVIDIA-Optimized Journeys
Explore NVIDIA-optimized support for Gemma’s small language models. Find several TensorRT-LLM optimized Gemma-2B and Gemma-7B model checkpoints on NGC. These include pre-trained and instruction-tuned versions suitable for running on NVIDIA GPUs, including consumer-grade RTX systems.
Optimized FP8 Quantized Version
Coming soon, experience the TensorRT-LLM optimized FP8 quantized version of the model in the Optimum-NVIDIA library on Hugging Face. Integrate fast LLM inference with just one line of code.
Deploy with NVIDIA NeMo Framework
Developers can use the NVIDIA NeMo framework to customize and deploy Gemma in production environments. NeMo supports popular customization techniques like supervised fine-tuning and parameter-efficient fine-tuning with LoRA and RLHF. It also offers 3D parallelism for training. Review the notebook to start coding with Gemma and NeMo.
Frequently Asked Questions About Gemma and TensorRT-LLM
1. What is Gemma, and how does it differ from previous models?
Gemma is a newly optimized family of open models created by Google, leveraging research and technology from the Gemini models. It offers enhanced performance and efficiency compared to previous iterations.
2. What role does TensorRT-LLM play in accelerating Gemma models?
TensorRT-LLM is an open-source library for optimizing inference performance. It significantly improves the speed and efficiency of Gemma models through various optimizations and kernels.
3. How does Gemma support real-time performance, and what are its implications?
Gemma, accelerated by TensorRT-LLM and NVIDIA H200 Tensor Core GPUs, achieves over 79,000 tokens per second on the Gemma 2B model. This level of real-time performance enables high-throughput inference for a wide range of applications.
4. Where can developers access Gemma models optimized with TensorRT-LLM?
Developers can find optimized Gemma-2B and Gemma-7B model checkpoints, including pre-trained and instruction-tuned versions, on the NVIDIA NGC platform. These models are compatible with NVIDIA GPUs, including consumer-grade RTX systems.
5. What is the significance of the TensorRT-LLM optimized FP8 quantized version of Gemma?
The TensorRT-LLM optimized FP8 quantized version of Gemma provides enhanced speed and efficiency, allowing for faster inference with reduced memory footprint. It will be available in the Optimum-NVIDIA library on Hugging Face.
6. How can developers customize and deploy Gemma models in production environments?
Developers can utilize the NVIDIA NeMo framework to customize and deploy Gemma models. NeMo supports various customization techniques, including supervised fine-tuning, parameter-efficient fine-tuning with LoRA, and reinforcement learning from human feedback (RLHF).
7. What are the safety features integrated into Gemma models?
Gemma models prioritize safety through extensive data curation, PII filtering, and reinforcement learning from human feedback. These measures ensure responsible AI practices and protect sensitive information.
8. How does Gemma contribute to the advancement of AI applications?
Trained on over six trillion tokens, Gemma enables developers to build and deploy high-performance, responsible, and advanced AI applications confidently. Its efficiency and scalability make it a valuable tool for various industries.
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