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 LLM inference slow and expensive?

Because generation is autoregressive, producing one token at a time with each token depending on all the previous ones, so a 500-token answer runs the full model 500 times in sequence. Each decode step is memory-bound, reading all the model's weights out of VRAM to make a single token, so memory bandwidth rather than compute is the bottleneck.

What is the KV cache and why does it matter?

The KV cache stores the key and value each token produces so attention is not recomputed over the whole history every step, which would be quadratically slow. It lives in VRAM and grows with every token for every concurrent request, so it can use more memory than the weights and is usually what limits how many users one GPU can serve.

What is continuous batching?

Continuous batching, popularized by vLLM, rebuilds the batch on every decode step so a finished sequence leaves immediately and a new request joins on the next step, keeping the GPU near full utilization. It replaces static batching, where a short reply is stuck waiting for the longest one in the group while its GPU slot sits idle.

Does quantization hurt LLM quality?

It depends how far you push it: INT8 is usually a drop of well under one percent and safe to default to, while INT4 costs a few percent on the hardest reasoning, math, and code tasks but stays fine for chat and summarization. Calibration methods like GPTQ and AWQ keep the weights that matter most in higher precision, and you should always measure accuracy on your own task.

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

A small draft model guesses the next few tokens and the big target model verifies them all in one parallel pass, so every guess the target agrees with is nearly free. At the first disagreement it uses the target's own token, so the result is bit-for-bit identical to running the big model alone, typically 2 to 3 times faster on predictable text.

How does batch size affect latency and throughput?

Batch size trades per-user speed for total efficiency. A larger batch reuses each weight read across more sequences, so aggregate tokens per second climb, but every user's own streaming speed drifts down as sequences share each pass. Interactive chat caps the batch to protect per-user speed, while offline jobs push it to the limit for the cheapest bulk tokens.

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. On a roofline this puts decode on the sloped, bandwidth-limited side, which is exactly 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.

On an 80 GB GPU serving a 13B model, why does the KV cache, not the weights, usually decide how many users you can serve?

The weights are a fixed one-time cost while the KV cache grows with every token for every concurrent user, so it overtakes the weights at only a handful of users, Model weights load once and stay fixed, but the KV cache is roughly a megabyte per token per user and grows with context length and concurrency. At about four users of long context it already exceeds the weights, so you run out of room to hold everyone's cache long before compute becomes the limit. That is why VRAM, managed by PagedAttention, sets capacity.

LLM Inference Optimization: Serving More Tokens Per GPU

Name: System Design Masterclass
Availability: InStock

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