Deploying open-source LLMs (like Llama-3) or real-time Whisper transcription in production often...
Deploying open-source LLMs (like Llama-3) or real-time Whisper transcription in production often forces a difficult architectural trade-off: keep dedicated GPUs running 24/7 (expensive) or rely on serverless scale-to-zero (cold start latency penalty).
To evaluate container spin-up overhead, we benchmarked median cold start latencies and per-second execution costs across the major serverless GPU platforms.
Benchmark Results
Provider
Median Cold Start
Nine serverless GPU platforms compared on specs, cold starts, and real pricing for AI inference in 2026, plus the one I'd…
When building 24/7 autonomous daemon agents and real-time LLM-driven game loops, API bills from...
I saw a developer asking on Reddit if there was any “sane way” to manage Cloud Run cold starts for AI...
You want to run Llama 3. You don't want to set up a server. You don't want to manage GPUs. You don't...
Last month I needed to stand up a Llama 3 inference endpoint for an internal tool. The requirements...
AWS gives you three ways to run LLM inference in production. I've deployed all three for clients and...
