#aws ec2

a detailed comparison between AWS EC2 **G6**, **G6e**, and **G6f** instance families, based on the most recent official specifications: --- ### **Overview Table** | Feature | **G6** | **G6e** (latest) | **G6f** (fractional GPU variant of G6) | | -------------------------- | ------------------------------------------ | ----------------------------------------------------- | ---------------------------------------------- | | **GPU Type** | NVIDIA L4 Tensor Core | NVIDIA L40S Tensor Core | NVIDIA L4 Tensor Core (fractionalized) | | **GPU Memory** | 24 GiB per GPU; fractional as low as 3 GiB | 48 GiB per GPU | 3 GiB (1/8 GPU) up to full GPU (24 GiB) | | **CPU** | 3rd-gen AMD EPYC 7R13 | 3rd-gen AMD EPYC 7R13 | Same as G6 | | **vCPU / RAM Range** | Up to 192 vCPU, 768 GiB RAM | Up to 192 vCPU, 1,536 GiB RAM | Similar to G6, tailored for fractional GPUs | | **Network Bandwidth** | Up to 100 Gbps | Up to 400 Gbps | Up to 100 Gbps | | **Local NVMe Storage** | Up to \~7.52 TB | Up to \~7.6 TB | Same capabilities as G6 | | **Performance Advantages** | \~2× inference & graphics boost vs G4dn | Up to 2.5× better than G5; high memory bandwidth | Same as G6; fractional for cost optimizations | | **Primary Use Cases** | ML inference, real-time graphics/rendering | Large LLM inference, generative AI, spatial computing | Lightweight inference, graphics cost-effective | --- ### **Key Highlights by Family** #### **G6** * Features NVIDIA L4 Tensor Core GPUs (24 GiB each) and fractional GPU options (down to 1/8 GPU, or 3 GiB) for cost-effective, scaled usage.([Amazon Web Services, Inc.][1], [Amazon Web Services, Inc.][2], [Databricks][3], [Amazon Web Services, Inc.][4]) * Offers up to 192 vCPUs, 100 Gbps networking, and \~7.52 TB NVMe storage.([Amazon Web Services, Inc.][1]) * Delivers roughly twice the performance for deep learning inference and graphics compared to G4dn instances.([Amazon Web Services, Inc.][1]) #### **G6e** * The latest iteration, leveraging NVIDIA L40S Tensor Core GPUs with 48 GiB memory per GPU and up to 8 GPUs per instance.([Amazon Web Services, Inc.][2]) * Provides up to 192 vCPUs, 400 Gbps networking, 1.536 TB system memory, and \~7.6 TB NVMe storage.([Amazon Web Services, Inc.][4]) * Offers **up to 2.5× better performance** compared to G5 instances and is targeted at large-scale inference, generative models, and spatial computing.([Amazon Web Services, Inc.][4]) #### **G6f** * Essentially a fractional GPU variant of G6: same NVIDIA L4 GPUs, but size options include fractions like 1/8, 1/4, etc.([Amazon Web Services, Inc.][2]) * Ideal for workloads that don’t need full GPU power and aim for cost efficiency.([Amazon Web Services, Inc.][1]) * Same CPU, memory, and bandwidth capabilities as G6 fully accompanied options.([Vantage][5]) --- ### Summary Insights * **Choose **G6** for balanced GPU compute & graphics workloads**, especially if you're working with smaller ML inference tasks or real-time rendering, and want flexibility in scaling down GPU usage. * **Opt for **G6e** when you need maximum GPU memory and bandwidth**, e.g., running large LLM inference or generative AI tasks, or spatial computing that benefits from higher throughput and memory. * **Use **G6f** when you're optimizing costs** and your workload can function with fractional GPU capacity—great for light inference workloads with lower memory requirements. Would you like a size-level breakdown (e.g., xlarge, 12xlarge) or price estimates in your region? Happy to dig deeper! [1]: https://aws.amazon.com/ec2/instance-types/g6/?utm_source=chatgpt.com "Amazon EC2 G6 Instances" [2]: https://aws.amazon.com/ec2/instance-types/?utm_source=chatgpt.com "Amazon EC2 Instance Types - Compute" [3]: https://www.databricks.com/blog/aws-ec2-g6?utm_source=chatgpt.com "Announcing Databricks Support for Amazon EC2 G6 ..." [4]: https://aws.amazon.com/ec2/instance-types/g6e/?utm_source=chatgpt.com "Amazon EC2 G6e Instances" [5]: https://instances.vantage.sh/aws/ec2/g6.xlarge?utm_source=chatgpt.com "g6.xlarge pricing and specs - Vantage"