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Amazon EC2 G7e instance family

Definition

Amazon EC2 G7e is the GPU-memory-optimised inference instance family in EC2, built around the NVIDIA RTX PRO 6000 Blackwell GPU with 96 GB of GPU memory. AWS positions G7e as a cost-efficient option for serving GPU-memory-intensive generative AI video models. The wiki sees G7e in production at Synthesia, where it hosts in-house latent-diffusion video generation models whose VRAM footprint scales with both model size and the size of decoded video chunks held briefly in GPU memory before D2H transfer.

The smallest size in the wiki-attested benchmarks is g7e.2xlarge — 1 GPU, on-demand price $3.36 / GPU / hour in us-east-2 (Ohio) at time of writing.

Why G7e (vs other GPU instance families)

  • VRAM, not raw FLOPs, is often the binding constraint for generative AI video. Latent-diffusion video models do iterative denoising in compressed latent space, then decode back to pixel space via a VAE decoder. The pixel-space intermediate is large; even with chunked temporal decoding (one latent frame → ~4 pixel frames per chunk), the activation + buffer + model-weights footprint can exceed the 80 GB ceiling on H100-SXM and the 48 GB ceiling on L40S. 96 GB on the RTX PRO 6000 Blackwell is enough headroom to run end-to-end inference on a single GPU without sharding the model.
  • Cost-efficient for VRAM-bound inference. Compared to H100-class training-grade GPUs, G7e trades pure-compute peak for more VRAM at a lower price point. AWS frames G7e as the right default for VRAM-bound generative video inference rather than for frontier-LLM training. (Source: sources/2026-05-19-aws-how-synthesia-optimizes-generative-ai-video-inference-on-amazon-ec2-g7e-instances.)
  • Customer-controlled hardware. AWS notes that customers like Synthesia choose EC2 over managed inference for "the flexibility and control over the underlying hardware that the service provides" — relevant when low-level CUDA-stream and pinned-memory tuning is part of the optimisation playbook.

Wiki-attested workload shape

  • Latent-diffusion video generation with a per-frame VAE decoder (Synthesia in-house models, plus public-benchmark Wan 2.2 14B Diffusers).
  • Chunked temporal decoding: each latent frame produces ~4 pixel frames; the chunk must transit GPU → host → storage between consecutive kernel launches, which becomes the binding bottleneck.
  • Asynchronous Frame Generation Pipeline as the software-side fix: dual CUDA streams + double pinned host buffers + worker thread + CUDA-event barriers lift the GPU's kernel utilisation from 82% → 99.9% on g7e.2xlarge running the Wan 2.2 14B VAE decoder.
  • Real Time Factor (decode-time / video-duration) baseline 3.21, post-optimisation 2.95.
  • Theoretical saving of ~$896 per 1,000 hours decoded video per GPU at the published g7e.2xlarge price.

Where G7e sits in the AWS GPU instance ladder

The wiki's GPU-instance coverage is incomplete but the G7e niche is:

  • G7e — RTX PRO 6000 Blackwell, 96 GB; VRAM-bound generative-AI inference (video, latent-diffusion).
  • P5 (H100) — H100, 80 GB; frontier-LLM training + large-model inference. (Most-cited training instance family in the wiki.)
  • G6e (L40S) — L40S, 48 GB; cheaper inference for models that fit in 48 GB.
  • P5e (H200), P6 (B200), P6e (GB200) — frontier training, not yet wiki-attested in this corpus.

The choice of G7e for Synthesia's workload is a VRAM-first decision, not a peak-FLOPs decision.

Key architectural primitives required

To get from the 82% baseline to the 99.9% post-optimisation kernel utilisation, a G7e workload must use:

  • Two CUDA streams — a Compute Stream and a dedicated Copy Stream — to overlap compute kernels with D2H copies on the GPU's separate copy engine.
  • Pinned (page-locked) host buffers — required for fully-async D2H DMA without staging through a bounce buffer.
  • Double buffering — two VRAM buffers + two pinned host buffers — so adjacent chunks operate on distinct memory regions.
  • CUDA events as cross-stream barriers — to ensure the worker thread doesn't read a half-written buffer.
  • A dedicated worker CPU thread for host-side I/O (file writes), so the main Python thread can keep launching kernels.

See patterns/asynchronous-frame-generation-pipeline for the full assembly.

Seen in

  • sources/2026-05-19-aws-how-synthesia-optimizes-generative-ai-video-inference-on-amazon-ec2-g7e-instances — first wiki canonicalisation of G7e as a named system. Synthesia hosts in-house latent-diffusion video generation models on G7e for VRAM headroom (96 GB Blackwell). Benchmark on g7e.2xlarge: unoptimised Hugging Face Diffusers Wan 2.2 14B VAE decoder, 41 latent frames, 10 consecutive runs after warmup. Synchronous baseline mean 21.99 s/video, async optimised mean 20.17 s/video (−8.2%); kernel utilisation 82% → 99.9%; RTF 3.21 → 2.95; on-demand price $3.36 / GPU / hour in Ohio.
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