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Most teams stay on managed APIs. Some discover their workload is too high-volume, too cost-sensitive, or too privacy-bound to keep paying per token. This course is the production self-serve path: quantize the model, optimize inference, deploy on Kubernetes, and prove it survives a 100K-query stress test.
Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.
Engineers are learning here from
Self-host an LLM that holds up under 100K parallel queries. Quantize the weights with W4A16 and W8A8, optimize KV cache and speculative decoding, wrap it in FastAPI, containerize it with Docker, and deploy on a GCP-hosted Kubernetes cluster. A guided curriculum that walks the production path from a baseline 3B model to a stress-tested service.
When the API bill outgrows a managed provider, you self-host. Here is the production path that actually scales.
What you'll ship
What you'll learn
Curriculum
The serving cost problem
Why teams self-host, what a baseline 3B model actually costs in latency and memory, where each optimization fits.
Weight quantization
W4A16, W8A8, W4A8 trade-offs measured on real evaluation data. Pick the right precision for your accuracy budget.
Inference optimization
KV cache, paged attention, prompt-lookup decoding, speculative decoding. Each one cuts latency at long inputs.
Production deployment
FastAPI server, Docker image, Kubernetes deployment plus HPA, GCP cluster setup, and a 100K-query Locust stress test.
Who it's for
The finance team wants to know why your inference cost grew 3x this quarter. You know self-hosting is cheaper at your volume, you just have not figured out the production path.
Your data cannot leave the VPC, or your latency budget cannot tolerate the network hop to a managed provider. You need self-hosting that actually scales.
Multiple teams want to deploy fine-tuned models. You need a reference architecture for FastAPI + GPU + Kubernetes that just works.
FAQ
No. The patterns scale up and down. Run the same setup at 100 queries per second on a single GPU, or scale to many replicas with HPA. The 100K test exists to prove the architecture, not as a requirement.
Yes. The Docker image and Kubernetes manifests are cloud-agnostic. The GCP-specific section is one short module on cluster creation. EKS or AKS need slightly different cluster commands but the rest is identical.
For the labs, a single A10G or L4 GPU is enough for a 3B model after W4A16 quantization. Production multi-GPU is covered in the deployment module but not required to follow along.
Enterprise RAG focuses on scaling RAG pipelines with Ray. This course focuses on serving the model itself: quantization, KV cache, FastAPI, K8s. The two are complementary; this is the model-serving layer that sits underneath the RAG layer.
Pricing
One subscription unlocks every paid course and workshop replay. Pick yearly or monthly.
Unlock with Pro
You save 47% with regional pricing
Billed annually. Cancel anytime.
Still deciding? Ask Param a question
Self-hosting is not magic. It is a stack of optimizations, each one quantified.
Serving LLMs at scale with vLLM, quantization, and Kubernetes
From $16/mo with Pro