Is this a video course?

No. This is an interactive, slide-based learning platform. Each lesson has rich text, animated diagrams, live code editors, and quizzes. You learn by reading, interacting, and doing, not by watching videos passively.

How long do I have access?

Forever. Both pricing tiers are one-time payments with lifetime access. This includes all current 766 lessons and any future content we add.

What level of experience do I need?

None. We start from absolute basics like 'What is latency?' and build up to distributed consensus protocols. The Foundation level assumes zero prior knowledge of system design.

How much does the system design course cost?

7.99 US dollars for lifetime access globally, or 499 Indian rupees for lifetime access in India. One-time payment, no subscription, no hidden fees. 11 lessons are free with no signup required.

What technologies are covered?

Everything from DNS and load balancers to Kubernetes, Kafka, distributed databases, consensus protocols, stream processing, security architecture, and observability. We cover principles and real-world implementations used at Netflix, Google, Amazon, Uber, Stripe, and more.

Is this useful for system design interview preparation?

Yes. The lessons are structured around the exact topics asked in system design interviews at FAANG and top-tier companies. Interactive diagrams help you practice whiteboard-style explanations. Covers everything from URL shortener design to distributed payment systems.

How is this different from ByteByteGo or Educative?

766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

Why must a model's weights fit in the GPU's VRAM for serving?

Because the weights are loaded into VRAM once and stay resident for the life of the replica, so VRAM sets the hard limit on model size, A serving replica loads the model into VRAM when it boots and keeps it there, rather than streaming it per request. That makes each card's VRAM the hard ceiling on how large a model, plus its activations and KV cache, you can serve. Run out and the process dies with an out-of-memory error.

Why should a GPU serving autoscaler scale on GPU utilization or queue depth rather than CPU usage?

GPU work does not show up in CPU usage, so a fully saturated GPU can look idle to CPU-based autoscaling and never trigger a scale-up, Inference runs on the GPU, and a card can be pinned at 100 percent while the host CPU sits nearly idle. If you autoscale on CPU, the signal never rises, so you never add replicas even as the GPU queue backs up. You scale on real GPU utilization (for example DCGM_FI_DEV_GPU_UTIL) or on request queue depth instead.

What is the trade-off that dynamic batching makes?

It adds a few milliseconds of latency per request (from the wait window) in exchange for running many requests in one GPU pass, greatly increasing throughput, A GPU running one request leaves most of its cores idle. The batcher holds requests for a short window (a few milliseconds), forms a batch, and runs them together in one forward pass, so several requests cost almost the same GPU time as one. The price is the small added latency from waiting to fill the batch, which you tune with a max batch size and max wait.

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Aryan from Bengaluru unlocked lifetime access · 3 minutes ago

Scaling and GPU Infrastructure: Serving Models Without Burning Money

Name: System Design Masterclass
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