Aug 18, 2024 6 min read Articles

Google Cloud Requires Overpayment for L4 GPUs

GCP is making users pay a hefty premium for L4 GPUs by imposing rigid hardware requirements. Discover how you could be wasting thousands of dollars on unnecessary compute resources and learn proven strategies to slash your cloud costs without sacrificing performance.

NVidia L4 GPU, available on GCS (Google Cloud Services)

Currently, the only available machine type for hosting an L4 GPU is the G2. While this isn't inherently problematic, the mandatory minimum configuration of 4 vCPUs and 16GB RAM for G2 instances can lead to significant overprovisioning and increased costs for many users. Let's discuss further.

Optimizing L4 GPU Costs on Google Cloud Platform

Background and Context

Since its release, I loved the NVIDIA L4, as it was perfect for my needs – 24GB RAM, and far better than the T4 for video transcoding as the L4 Tensor Core GPU is generally superior to the T4 for video transcoding tasks. The L4 GPU improves AI experiences with around 3x more generative AI performance than the previous generation. It can run over 1,000+ simultaneous video streams and has 4x better performance on AI processing and video.

Comparing the NVIDIA T4 to the NVIDIA L4 side-by-side for video transcoding performance.

I've seen a tremendous increase in Livepeer network transcoding traffic over the past 24-48 hours, and I'd like to be well-poised to take advantage. The AI subnet is fast developing. When perfected, it's going to be a massive boost in the power of video decentralization and its abilities, specifically cost reduction and ease of use. Extremely exciting.

The logistical flow chart shows the process of video transcoding in realtime -- from input to output.

Chart showing the L4 having up to 120x more AI video performance than a multi-CPU server.

I'm big on cost reduction. Especially since we don't want excessive costs passed along to Creators or Delegators; reduction in costs = increased Delegator return. The more Delegators per Orchestrator, the more transcoding work, effectively yielding increased $ETH and $LPT payouts, returns, and overall ROI.

I began comparing and analyzing the all-in costs for resource commitments and off-site redundancy (which serves as a backstop in the unexpected case of frivolous or malicious attacks, as we believe Creators should speak freely and within the bounds of local laws).

Ultimately, we'll end up overpaying on GCP – for now – until options are expanded.

This allows us to increase return on Livepeer Orchestrators and Pools, while (most importantly) delivering a higher yield to Delegators, who then earn greater fees in both $ETH and $LPT.Would love to hear from folks with similar predicaments or cloud architecture suggestions. Feel free to send it my way: andrew@heytalbot.com

Technical Data and The GCP Problem

NVIDIA T4 vs L4: It's Like Trading in Your Bicycle for a Rocket Ship (For AI, at Least)

Let's be honest, comparing the NVIDIA T4 and NVIDIA L4 for AI and transcoding workloads is a little like comparing a seasoned chef with a microwave meal. Both can get the job done, but one is clearly going to offer a more sophisticated (and faster!) experience. To illustrate this point, feast your eyes on the delicious data below:

Chart shows various key features, side-by-side, comparing the NVIDIA T4 GPU with the NVIDIA L4 GPU. This highlights the tremendous power capabilities and improvements of the L4 -- with similar power consumption.

Key Takeaways (aka The TL;DR for the Impatient):

Faster, Faster, AI!

The L4 boasts significantly higher performance for AI tasks thanks to its Ampere architecture and beefier Tensor Cores. Think of it as giving your AI models a serious caffeine boost.

Transcoding Turbocharged

The L4 can handle nearly double the video encoding and decoding streams compared to the T4. If you're working with video, the L4 is like having a whole team of video editors working around the clock.

Power Efficiency Champion

Despite the significant performance gains, the L4 manages to maintain similar power consumption to the T4. Who says you can't have your cake and eat it too?

The NVIDIA L4 Wins The Day

While the T4 is certainly a capable GPU, the L4 represents a substantial leap forward, especially for AI and transcoding workloads. If you're looking to seriously level up your processing power, the L4 is the clear winner.

So, what's the problem?

Google Cloud Platform (GCP) is a powerhouse and its array of VM configurations, including those equipped with powerful GPUs like the NVIDIA L4, offers immense potential. However, optimizing costs while maximizing performance can be a complex challenge. I'll describe the specific issue L4 GPUs face on GCP, then aim to provide actual strategies to mitigate costs.

Understanding the L4 GPU Conundrum on GCP

The NVIDIA L4 GPU is a versatile chip suitable for a wide range of workloads, from machine learning to video processing. Yet, GCP imposes specific constraints on its deployment. Currently, the only available machine type for hosting an L4 GPU is the G2. While this isn't inherently problematic, the mandatory minimum configuration of 4 vCPUs and 16GB RAM for G2 instances can lead to significant overprovisioning and increased costs for many users (it certainly does for me).

The Cost Implications of L4 GPU Overprovisioning

For workloads primarily reliant on GPU power, the excess CPU and RAM resources bundled with the L4 GPU on GCP's G2 instances translate to unnecessary expenses. This overprovisioning becomes even more pronounced when considering Committed Use Discounts (CUDs), where users are locked into a minimum configuration for a three-year term.

Strategies to Optimize L4 GPU Costs on GCPIn-Depth Workload Analysis:

Identify GPU Utilization: Determine the extent to which your workload relies on GPU acceleration.
Assess CPU and RAM Requirements: Evaluate the actual CPU and RAM demands of your application.
Rightsizing Instances: Match your instance configuration as closely as possible to your workload's needs to avoid overprovisioning.

Explore Alternative GPU Options

Consider T4 or A100 GPUs: These options might offer more flexible configuration choices or better performance-to-cost ratios for specific workloads.

For me? This wasn't a feasible option, as I'm focused solely on maximizing transcoding and AI workloads across the globe. My goal is to maximize output for Delegators, who are essentially investors of the Livepeer Network. But for others, it might be a better solution.Evaluate Performance Benchmarks: Conduct thorough testing to determine the most suitable GPU for your application.

Leverage Spot Instances and Preemptible VMs

Cost Savings: Take advantage of significant discounts offered by spot instances and preemptible VMs.
Workload Tolerance: Ensure your application can handle interruptions associated with these instance types.
Custom Machine Types (When Available); Flexibility: Keep an eye on GCP's roadmap for the introduction of custom machine types, which could allow for more granular resource allocation.

Effective Use of Committed Use Discounts:

Accurate Forecasting: Carefully predict your future resource needs to avoid overcommitting.
Rightsizing Commitments: Align your CUD with expected workload growth.

Continuous Monitoring and Optimization

Real-time Insights: Utilize GCP's monitoring tools to track resource utilization.
Dynamic Adjustments: Make necessary changes to instance configurations based on performance metrics.

So, Let's Conclude

Optimizing L4 GPU costs on GCP requires a strategic approach that combines in-depth workload analysis, careful consideration of alternative options, and the effective use of GCP's cost-saving features. By following these guidelines, organizations (like ours, at both Livepeer.com and Leapjuice.com can significantly reduce expenses without compromising performance.

As GCP's infrastructure evolves, staying informed about new offerings and best practices will be crucial for maintaining cost efficiency.

It certainly has for me!