How do AWS Inf2 and G5 instances compare for AI inference?

This article compares the cost, throughput, memory architecture, and software ecosystems of Amazon EC2 Inf2 instances powered by AWS Inferentia2 chips and GPU-based G5 instances powered by NVIDIA A10G Tensor Core GPUs. It explores how architectural differences affect performance across large language models, computer vision, and generative AI workloads, while providing formulas and metrics to help engineering teams select the most cost-effective instance family for production inference.

Architectural Foundations: AWS Inferentia2 vs. NVIDIA A10G

Selecting the right compute instance for artificial intelligence inference on Amazon Web Services often comes down to comparing purpose-built silicon against general-purpose graphics processing units. Amazon EC2 Inf2 instances utilize AWS Inferentia2 accelerators, which are application-specific integrated circuits designed exclusively for deep learning inference. In contrast, Amazon EC2 G5 instances feature NVIDIA A10G Tensor Core GPUs, built on the NVIDIA Ampere architecture, which support both training, graphics rendering, and inference.

Each Inferentia2 chip contains two NeuronCores-v2, each equipped with a dedicated tensor engine, vector engine, scalar engine, and digital signal processing engine. A defining characteristic of the Inf2 architecture is its use of High Bandwidth Memory (HBM2e). Every Inferentia2 accelerator includes 32 GB of HBM2e delivering 820 GB/s of memory bandwidth per chip. Additionally, Inf2 instances feature NeuronLink-v2, a high-speed chip-to-chip interconnect that bypasses the host CPU and PCIe bus, allowing multiple accelerators on larger instances like the inf2.48xlarge to pool memory and execute distributed tensor parallelism with minimal latency.

G5 instances rely on the NVIDIA A10G GPU, which provides 24 GB of GDDR6 memory per accelerator with 600 GB/s of memory bandwidth. While GDDR6 is cost-effective and versatile, it offers roughly 27 percent less memory bandwidth per accelerator than the HBM2e found on Inferentia2. On multi-GPU G5 instances such as the g5.12xlarge or g5.48xlarge, inter-GPU communication occurs over PCIe Gen4 rather than dedicated NVLink bridges, which introduces higher communication overhead when sharding large models across multiple GPUs.

Pricing and Hourly Cost Comparison

On-Demand pricing in the US East (N. Virginia) region reveals a consistent cost advantage for Inf2 instances over comparably sized G5 instances, alongside higher total accelerator memory at the entry and maximum tiers.

At the single-accelerator tier, inf2.xlarge costs approximately 25 percent less per hour than g5.xlarge while offering 33 percent more accelerator memory (32 GB vs. 24 GB). That extra 8 GB of memory is critical for large language model deployment: a 13-billion-parameter model quantized or cast to 16-bit floating point (FP16 or BF16) requires roughly 26 GB of memory just to hold the weights, fitting onto a single inf2.xlarge instance while requiring either quantization to 8-bit/4-bit or upgrading to a multi-GPU instance on the G5 family.

At the top end of the lineup, inf2.48xlarge provides 384 GB of total HBM2e memory across 12 accelerators for $12.981 per hour, whereas g5.48xlarge provides 192 GB of GDDR6 memory across 8 GPUs for $16.288 per hour. The Inf2 flagship delivers double the total accelerator memory at a 20 percent lower hourly rate.

Throughput and Latency Dynamics

Inference performance is typically governed by two distinct regimes: the compute-bound regime and the memory-bandwidth-bound regime. Understanding how Inf2 and G5 behave in both regimes clarifies where each instance excels.

For autoregressive large language models (such as Llama 3, Mistral, or Qwen), token generation consists of two phases:

1. Prefill (Prompt Processing): Processing input tokens in parallel. This phase is heavily compute-bound and depends on matrix multiplication throughput (measured in TFLOPS).
2. Decode (Token Generation): Generating one token at a time sequentially per request. This phase is memory-bandwidth-bound because every model parameter must be read from accelerator memory into the compute units for each generated token.

We can express the theoretical maximum token generation rate during the memory-bound decode phase for a batch size of 1 as:

\[\text{Throughput}_{\text{decode}} \approx \frac{B_{\text{mem}}}{P \times b_{\text{param}}}\]

Where:

• \(B_{\text{mem}}\) is the effective memory bandwidth in bytes per second.
• \(P\) is the number of active parameters in the model.
• \(b_{\text{param}}\) is the number of bytes per parameter (for example, \(2\text{ bytes}\) for \(\text{BF16}\) or \(\text{FP16}\)).

Applying this formula to a 7-billion-parameter model (\(P = 7 \times 10^9\)) running in 16-bit precision (\(b_{\text{param}} = 2\), totaling \(14\text{ GB}\) of weights):

\[\text{Theoretical Max (Inferentia2)} \approx \frac{820\text{ GB/s}}{14\text{ GB}} \approx 58.5\text{ tokens/second}\]

\[\text{Theoretical Max (NVIDIA A10G)} \approx \frac{600\text{ GB/s}}{14\text{ GB}} \approx 42.8\text{ tokens/second}\]

In real-world benchmarks utilizing continuous batching frameworks like vLLM or Hugging Face Text Generation Inference (TGI), practical utilization reaches 60 to 80 percent of theoretical bandwidth. Consequently, a single Inferentia2 chip on inf2.xlarge consistently achieves 30 to 40 percent higher single-stream token generation speed and higher aggregate throughput than an NVIDIA A10G on g5.xlarge for memory-bound workloads.

When scaling to larger models such as 70-billion-parameter LLMs across multiple accelerators using tensor parallelism, communication latency after every attention and feed-forward layer becomes a primary bottleneck. The total per-token latency \(T_{\text{token}}\) across \(N\) devices can be modeled as:

\[T_{\text{token}} = \frac{P \times b_{\text{param}}}{N \times B_{\text{mem}}} + L_{\text{comm}}(N)\]

Where \(L_{\text{comm}}(N)\) represents the all-reduce synchronization overhead across \(N\) accelerators. Because inf2.24xlarge and inf2.48xlarge use dedicated NeuronLink-v2 rings rather than shared PCIe Gen4 switches, \(L_{\text{comm}}(N)\) is significantly lower on Inf2 than on g5.12xlarge or g5.48xlarge. For 70B models, Inf2 instances frequently achieve up to 2x to 3x higher throughput than multi-GPU G5 instances.

Calculating Price-Performance Efficiency

To evaluate the financial impact of throughput differences, engineering teams normalize hourly instance cost against throughput using the cost per million tokens (for text models) or cost per million inferences (for vision and embedding models):

\[\text{Cost per } 10^6 \text{ Tokens} = \left( \frac{C_{\text{hour}}}{3600 \times \text{TPS}} \right) \times 10^6\]

Where:

• \(C_{\text{hour}}\) is the instance hourly price in dollars.
• \(\text{TPS}\) is the sustained total throughput in tokens per second across concurrent requests.

Consider a production deployment of an 8-billion-parameter model in BF16 serving concurrent traffic with continuous batching:

• On g5.xlarge (\(C_{\text{hour}} = \$1.006\)), sustained throughput under moderate concurrency might reach \(\text{TPS} = 450\text{ tokens/s}\).
• On inf2.xlarge (\(C_{\text{hour}} = \$0.758\)), sustained throughput under identical latency service-level agreements (SLAs) might reach \(\text{TPS} = 600\text{ tokens/s}\).

Calculating the cost per million tokens for each:

\[\text{Cost}_{\text{G5}} = \left( \frac{1.006}{3600 \times 450} \right) \times 10^6 \approx \$0.621\text{ per } 10^6\text{ tokens}\]

\[\text{Cost}_{\text{Inf2}} = \left( \frac{0.758}{3600 \times 600} \right) \times 10^6 \approx \$0.351\text{ per } 10^6\text{ tokens}\]

By combining a 25 percent lower hourly cost with a 33 percent higher throughput, inf2.xlarge lowers the effective cost per token by roughly 43 percent. Across vision transformers, BERT-style encoders, and diffusion models (such as Stable Diffusion XL), AWS benchmarks and independent testing consistently show 35 to 50 percent lower cost per inference on Inf2 compared to G5.

Data Type Support and Numerical Precision

Hardware support for numerical data types dictates how models are quantized and executed:

• AWS Inferentia2 natively accelerates FP32, TF32, BF16, FP16, INT8, and configurable floating-point8 (cFP8). Furthermore, the AWS Neuron compiler automatically casts FP32 models to BF16 by default, maintaining the dynamic exponent range of 32-bit floats while matching the throughput of 16-bit operations.
• NVIDIA A10G natively accelerates FP32, TF32, BF16, FP16, INT8, and INT4. Because the A10G is based on the Ampere architecture (rather than Hopper or Ada Lovelace), it lacks native hardware FP8 Tensor Cores, though it benefits from mature software ecosystems for INT4 weight-only quantization formats such as AWQ and GPTQ.

Software Ecosystem and Compilation Trade-Offs

While Inf2 holds a clear lead in raw hardware metrics per dollar, G5 instances maintain a substantial advantage in developer ergonomics and immediate compatibility.

G5 instances run standard NVIDIA CUDA, cuDNN, and TensorRT stacks. Any model written in PyTorch, JAX, or TensorFlow with custom CUDA kernels (such as FlashAttention, custom RoPE implementations, or dynamic control flow) runs out of the box on G5 without ahead-of-time compilation.

Inf2 instances require the AWS Neuron SDK (torch-neuronx, transformers-neuronx, or Optimum Neuron). Running a model on Inferentia2 involves an ahead-of-time (AOT) graph compilation step using the Neuron Compiler (neuronx-cc):

1. Static Input Shapes: Models compiled for Inferentia2 must have fixed batch sizes, sequence lengths, or bucketed context lengths defined at compile time. If traffic patterns change and require a new maximum sequence length, the model graph must be recompiled.
2. Compilation Time: Compiling a 7B to 70B LLM can take anywhere from 15 minutes to over an hour depending on host CPU resources, making rapid prototyping slower unless pre-compiled checkpoints from the Hugging Face Neuron Model Hub are used.
3. Operator Coverage: While standard Transformer architectures (Llama, Mistral, BERT, ViT, Stable Diffusion) have first-class support in optimum-neuron and vLLM's Neuron backend, bleeding-edge architectures or custom C++/CUDA extensions require porting to Neuron Custom C++ Operators or Neuron Kernel Interface (NKI).

Summary Decision Matrix

Choosing between Amazon EC2 Inf2 and G5 instances depends on balancing operational maturity against unit economics:

• Choose Amazon EC2 Inf2 when:
  • You are running high-volume, steady-state production inference where reducing cost per inference by 40 to 50 percent directly impacts margins.
  • You are deploying standard architectures (Llama, Mistral, Qwen, BERT, Whisper, Stable Diffusion) supported by Hugging Face Optimum Neuron or vLLM.
  • You need to fit larger models (such as 13B BF16 models on a single chip or 70B+ BF16 models on a single node) using Inf2's larger 32 GB per-chip HBM2e capacity and high-speed NeuronLink-v2 interconnect.
• Choose Amazon EC2 G5 when:
  • You are prototyping new or custom model architectures that rely on custom CUDA kernels, dynamic input shapes, or experimental quantization methods.
  • Your engineering workflow prioritizes zero-compilation deployment using standard Docker containers and upstream PyTorch or TensorRT-LLM builds.
  • Your workloads combine 3D graphics rendering, video transcoding, and deep learning inference on the same underlying instance fleet.