AWS g4dn.12xlargevsAWS g4dn.4xlarge

g4dn.12xlarge pairs 48 vCPUs with 192GB of RAM at $3.9120/hr On-Demand (about $2817/mo at 24×7). g4dn.4xlarge pairs 16 vCPUs with 64GB at $1.2040/hr (~$867/mo). g4dn.4xlarge is 69% cheaper per hour than g4dn.12xlarge ($2.7080/hr gap).

Because both instances are in the **g4dn family**, the only thing that changes between them is sizing — same silicon, same architecture (Intel Xeon (x86_64)), same burstable/sustained behavior. The choice is purely about how much capacity you actually need: g4dn.12xlarge gives you 48 vCPUs and 192GB of RAM, g4dn.4xlarge gives you 16 vCPUs and 64GB. AWS scales pricing close to linearly within a family, so picking the right size is mostly about right-sizing your workload, not getting a better deal per vCPU.

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 g4dn.12xlarge and pending for g4dn.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 g4dn.12xlarge when your workload is closer to GPU-accelerated (graphics + ML inference) (graphics workloads, video transcoding, ML inference). Pick g4dn.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.