AWS EC2 G6,G6e,G6f

a detailed comparison between AWS EC2 G6, G6e, and G6f instance families, based on the most recent official specifications:

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

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., Amazon Web Services, Inc., Databricks, Amazon Web Services, Inc.)
Offers up to 192 vCPUs, 100 Gbps networking, and ~7.52 TB NVMe storage.(Amazon Web Services, Inc.)
Delivers roughly twice the performance for deep learning inference and graphics compared to G4dn instances.(Amazon Web Services, Inc.)

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.)
Provides up to 192 vCPUs, 400 Gbps networking, 1.536 TB system memory, and ~7.6 TB NVMe storage.(Amazon Web Services, Inc.)
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.)

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.)
Ideal for workloads that don’t need full GPU power and aim for cost efficiency.(Amazon Web Services, Inc.)
Same CPU, memory, and bandwidth capabilities as G6 fully accompanied options.(Vantage)

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!