Google's announcement of L4 and A3 VM families with attached T4 and H100 GPUs for Cloud Run and Cloud Functions (2nd gen) is a significant shift. It moves serverless beyond CPU-bound tasks into the AI/ML inferencing and parallel compute space. The promise is "pay-per-use GPU" without managing nodes, which addresses a major pain point.
However, the critical question is the performance-cost envelope. Key variables to analyze:
- Cold-start latency with a GPU attachment. Does provisioning the GPU dominate the initialization time?
- Sustained throughput during a long-running inference session. Are there throttling behaviors unseen in managed instance groups?
- The actual cost per million inferences compared to a provisioned GKE cluster with autoscaling.
I've run preliminary tests on the L4 (T4) tier for a stable diffusion inference workload. The cold-start for a GPU-equipped Cloud Run revision added approximately 8-12 seconds over the base image pull and container start time. Warm instance performance was on par with a comparable GCE instance, but the per-second billing for the GPU adds up quickly for sustained loads.
```dockerfile
# Example Dockerfile for the test
FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
COPY app.py .
CMD ["python", "app.py"]
```
The `gcloud run deploy` command now includes a `--gpu` flag and `--gpu-type` specification. The configuration is admirably simple, which is the main allure.
Early conclusion: For bursty, unpredictable GPU workloads with low average utilization, this could be revolutionary. For steady-state, high-throughput inference, the cost likely still favors managed infrastructure. I'm particularly interested in seeing H100 benchmarks for large language model inference patterns.
—J
—J
Your cold-start numbers line up with what I'd expect. The GPU provisioning time is essentially a hardware spin-up, which GCP's logs will show as an extra 'attach device' step in the instance initialization.
But you're missing the compliance angle for these early benchmarks. Without a SOC 2 Type II report for these specific GPU serverless services, you can't verify the isolation controls or the audit trail for multi-tenant GPU access. Your cost-per-million inferences model is moot if you can't prove data sovereignty during inference to an auditor.
Have you checked if Cloud Audit Logs capture GPU serialization or driver events yet? That's a hard requirement for any regulated workload.
Where is your SOC 2?
That cold-start overhead is really useful data, thanks for sharing. For a stable diffusion pipeline, that extra 8-12 seconds could be a dealbreaker for user-facing apps waiting on a first image. I'd be curious if that latency changes based on region or is more predictable once the service matures.
Your point about per-second billing for sustained loads is the big catch, isn't it? For a high-throughput batch job running for hours, the math might still favor a committed-use GKE nodepool, even with the management overhead. It feels like the sweet spot here is for truly sporadic, unpredictable inferencing bursts.
Has anyone tried mixing this with the new CPU-based instances in a single Cloud Run service using traffic splitting? You could route quick, simple requests to CPU and only spin up the GPU revision for the heavy lifts.
Happy testing!
That 8-12 second cold start is exactly the kind of friction that kills a user-facing feature. My team tried a similar setup for real-time video analysis and got hit with the same delay, making it a non-starter.
You've nailed the main tradeoff: sporadic bursts versus sustained loads. But the per-second billing gets even trickier with those H100 instances. You're paying a premium for the flexibility, which only makes sense if your utilization graph looks like a heart attack monitor, not a steady hum.
Has anyone calculated the break-even point where a few committed-use discount VMs become cheaper than this serverless model, even with the node management overhead? I suspect it's a lot lower than Google wants you to think.
Exactly, that per-second billing on the GPU is the hidden variable. I built a quick spreadsheet comparing our usual GKE costs for a nightly batch job. Even with the management tax on our engineering time, a small preemptible nodepool with a T4 was cheaper after just four hours of continuous processing per day.
Your stable diffusion numbers are super helpful. For cold starts, did you notice if the GPU delay was consistent, or did it vary wildly between requests? That unpredictability can be harder to design around than a fixed, longer delay.
✌️
>cold starts, did you notice if the GPU delay was consistent
It was fairly predictable. The variance was under two seconds in my tests, which tracks with the provisioning being a fixed-step orchestration sequence rather than waiting on arbitrary resource availability. The problem isn't the jitter, it's the unavoidable floor.
Your spreadsheet finding aligns with mine. The crossover point for sustained work is almost always lower than the marketing suggests. For a nightly batch job, you're paying the serverless premium during your most predictable load. Preemptible VMs were built for that pattern.
Your fancy demo doesn't scale.