Accelerated & AI
AWS AI Inference Acceleration: G4dn vs G5 vs G6 vs Inf2
NVIDIA T4 vs A10G vs L4 vs AWS Inferentia2 for LLM and ML model serving
Inference economics dictate the success of generative AI applications. Compare NVIDIA Tensor Core GPUs (G4dn, G5, G6) with custom AWS Inferentia2 silicon on throughput and token cost.
1. Architectural & Silicon Specs Head-to-Head
Comparing underlying processor microarchitectures, memory ratios, and Nitro generation features using representative xlarge sizing.
g4dnx86_64
G4DN Accelerated Computing
ProcessorIntel Xeon Family
Clock Speed2.5 GHz
RAM / vCPU4.0 GiB/vCPU
Max Network50 Gbps
Max EBS IOPS40,000
AWS NitroSupported
Min Rate (g4dn.xlarge)$0.5260/hr
Monthly Est.$383.98/mo
Cost per vCPU$0.1315/hr
Cost per GiB RAM$0.0329/hr
CoreMark Benchmark55,463
CoreMark / $105,443
Price IndexLowest Cost
Save 48% vs most expensive
Compute / $ Value100% (Leader)
g5x86_64
G5 Accelerated Computing
ProcessorAMD EPYC 7R32
Clock Speed2.8 GHz
RAM / vCPU4.0 GiB/vCPU
Max Network100 Gbps
Max EBS IOPS80,000
AWS NitroSupported
Min Rate (g5.xlarge)$1.0060/hr
Monthly Est.$734.38/mo
Cost per vCPU$0.2515/hr
Cost per GiB RAM$0.0629/hr
CoreMark Benchmark69,634
CoreMark / $69,219
Price Index100% of highest
Compute / $ Value66% of leader
g6x86_64
G6 Accelerated Computing
ProcessorAMD EPYC 7R13 Processor
Clock Speed2.6 GHz
RAM / vCPU4.0 GiB/vCPU
Max Network100 Gbps
Max EBS IOPS240,000
AWS NitroSupported
Min Rate (g6.xlarge)$0.8048/hr
Monthly Est.$587.50/mo
Cost per vCPU$0.2012/hr
Cost per GiB RAM$0.0503/hr
CoreMark Benchmark83,814
CoreMark / $104,143
Price Index80% of highest
Save 20% vs most expensive
Compute / $ Value99% of leader
inf2x86_64
INF2 Accelerated Computing
ProcessorAMD EPYC 7R13 Processor
Clock Speed2.95 GHz
RAM / vCPU4.0 GiB/vCPU
Max Network100 Gbps
Max EBS IOPS240,000
AWS NitroSupported
Min Rate (inf2.xlarge)$0.7582/hr
Monthly Est.$553.49/mo
Cost per vCPU$0.1895/hr
Cost per GiB RAM$0.0474/hr
CoreMark Benchmark79,657
CoreMark / $105,061
Price Index75% of highest
Save 25% vs most expensive
Compute / $ Value100% of leader
2. Side-by-Side Sizing & Pricing Matrix
Direct price and benchmark comparison across matching instance tiers in the lowest-cost region. Lowest hourly cost in each row is highlighted in green.
| Size Tier | vCPU | Memory | g4dnLinux Rate | g5Linux Rate | g6Linux Rate | inf2Linux Rate |
|---|---|---|---|---|---|---|
| .xlarge | 4 | 16 GB | ||||
| .2xlarge | 8 | 32 GB | — | |||
| .4xlarge | 16 | 64 GB | — | |||
| .8xlarge | 32 | 128 GB | ||||
| .12xlarge | 48 | 192 GB | — | |||
| .16xlarge | 64 | 256 GB | — | |||
| .24xlarge | 96 | 384 GB | — | |||
| .48xlarge | 192 | 768 GB | — |
3. Workload Decision Framework
Clear, actionable rules on when each family wins based on workload profile, runtime language, and licensing boundaries.
Recommended:G6 (NVIDIA L4) or G5 (A10G)
When: Serving PyTorch/HuggingFace LLMs and diffusion models with standard CUDA software stacks
Why: Ada Lovelace architecture with FP8 precision support and extensive CUDA library maturity.
Recommended:Inf2 (Inferentia2)
When: High-volume production LLM serving where models can be compiled with AWS Neuron SDK
Why: Up to 50% lower cost per inference compared to comparable GPU instances.
Recommended:G4dn (NVIDIA T4)
When: Low-cost legacy inference or video transcoding pipelines
Why: Cost-effective baseline with hardware-accelerated video encoding/decoding.