AWS g6.8xlargevsAWS g6e.4xlarge

g6.8xlarge pairs 32 vCPUs with 128GB of RAM at $2.0144/hr On-Demand (about $1450/mo at 24×7). g6e.4xlarge pairs 16 vCPUs with 128GB at $3.0042/hr (~$2163/mo). g6.8xlarge is 49% cheaper per hour than g6e.4xlarge ($0.9898/hr gap).

Both are generation-6 GPU-accelerated (graphics + ML inference) instances, but they run on different silicon: **g6.8xlarge** is Intel Xeon (x86_64), **g6e.4xlarge** is Intel Xeon (x86_64). AMD variants (suffix `a`) are typically 10% cheaper than Intel siblings at comparable single-thread performance. Graviton variants (suffix `g`) are usually 20–40% cheaper but require ARM64-compatible binaries — most modern Linux stacks are fine, but verify any compiled extensions, native modules, or third-party binaries before migrating. Same vCPU/RAM ratio, same network performance class, different processor.

Benchmark data for at least one of these instances is still being collected, so a direct performance-per-dollar comparison isn't possible yet. Sysbench scores are pending for g6.8xlarge and pending for g6e.4xlarge. Check back as the benchmark queue completes — newer-generation instances typically score 10–30% higher on single-thread and 15–50% higher on multi-core vs the previous generation in the same series.

In practice, pick g6.8xlarge when your workload is closer to GPU-accelerated (graphics + ML inference) (graphics workloads, video transcoding, ML inference). Pick g6e.4xlarge when it's closer to GPU-accelerated (graphics + ML inference) (graphics workloads, video transcoding, ML inference). When neither side is obviously right, the cheaper hourly rate usually wins for fault-tolerant batch workloads, while the higher single-core score usually wins for latency-sensitive web traffic. The regional pricing tables linked from each instance page below show where each is currently cheapest — sometimes a >20% regional gap flips the comparison entirely.