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 is the decode phase of LLM inference described as memory-bound rather than compute-bound?

Because each step must read the entire model's weights out of VRAM to produce just one token, so bandwidth is the bottleneck, Generating one token requires reading all of the model's weights from VRAM. The GPU's math units sit mostly idle while it moves those bytes, so memory bandwidth, not compute, limits decode speed. This is why quantization and batching help so much.

What is the main advantage of continuous batching over static batching for LLM serving?

It rebuilds the batch every decode step, so finished sequences leave immediately and new ones join without waiting, keeping the GPU near full utilization, Static batching holds a whole group until the longest reply finishes, so short replies waste GPU slots. Continuous batching rebuilds the batch every step, freeing finished slots and admitting new requests instantly, which pushes utilization from roughly a third of the card to near full and multiplies throughput.

Why does speculative decoding speed up generation without changing the output?

A small draft model guesses several tokens, the big model verifies them in one pass, and at the first mismatch it uses its own token, so the result is identical to running the big model alone, The draft model proposes tokens and the target model verifies all of them in a single parallel pass. Any token the target agrees with is accepted for free, and at the first disagreement the target's own token is used. Because the fallback is always the target's own output, the final text is bit-for-bit identical, so there is no quality cost.

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Aryan from Bengaluru unlocked lifetime access · 3 minutes ago

LLM Inference Optimization: Serving More Tokens Per GPU

Name: System Design Masterclass
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in Llm Genai Ops

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