CompleteSystem Design

Phase 19 Interactive Simulators(5)

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🧠 GPU Inference Serving Lab — full guide →

Feature Store & GPU Inference Serving

Tune Triton dynamic batching and the vLLM KV-cache pool to see how GPU utilization, latency and VRAM capacity interact.

Assembled batch

4 rows

over 2ms window @ 2,000 QPS

GPU utilization

100%

Tensor Cores well fed

p50 latency

11.7 ms

queue + GPU + 3ms Feast fetch + 5ms net

H100s needed

2

$16.1 per 1M inferences

LLM KV-Cache pool (LLaMA-3 70B, GQA: 80 layers x 8 KV heads x d=128)

KV per token

0.625 MB

FP16

KV per session

2.68 GB

at 4,096 tokens

VRAM headroom

90 GB

2xH100 pool minus 70 GB weights

Max sessions

33

16-token blocks, on-demand

Batch size 1 leaves >85% of the H100 SMs idle: a 2ms window at 2,000 QPS assembles ~4-row batches and pushes utilization upward, while the offline Point-in-Time store keeps training identical to the online Redis features the gateway joins in <3ms. A single 4,096-token FP16 session consumes ~2.7 GB of VRAM, so legacy contiguous allocation collapses capacity long before PagedAttention does.

PHASE 19 CURRICULUM

Emerging Paradigms & Advanced Topics

Progress0 of 5 (0%)

Modern system architecture extends beyond classical stateless CRUD services.

Key Architectural Domains & Syllabus
High-scale tech companies operate complex production Machine Learning infrastructure with low-latency feature stores
two-stage candidate retrieval recommendation pipelines
globally distributed edge compute runtimes
cloud-agnostic disaster recovery systems
immutable cryptographically verifiable audit ledgers required for fintech compliance and quantum resilience
5 In-Depth Topics ~40 Minutes Reading Time Interactive Quizzes & Assessments

All Topics in Phase 19

0 of 5 completed
#276ML Systems Design: Feature Stores, Model Serving & Inference PipelinesAdvancedFREE

Architecting high-throughput, low-latency machine learning and LLM serving systems: unified online/offline feature stores (Feast/Hopsworks), dynamic batching with Triton Inference Server, KV-cache memory management (PagedAttention/vLLM), and silent data/concept drift detection.

16 min read•3 Quiz Questions
#277Real-Time Recommendation Systems & Vector Similarity SearchAdvancedFREE

Building multi-stage recommendation funnels at 100M+ catalog scale: Two-Tower dual encoders, Approximate Nearest Neighbor (ANN) vector indexing (HNSW, IVF-PQ, SCaNN), multi-task ranking with Mixture-of-Experts (MMoE), and sub-50ms business re-ranking.

16 min read•3 Quiz Questions
#278Edge Computing Architectures: V8 Isolates, WebAssembly & Distributed StateAdvanced PRO

Architecting sub-10ms global applications: Anycast BGP routing, V8 Isolates vs Containers, WebAssembly (Wasm/WASI) sandboxing, Edge KV, Distributed SQLite (D1/Turso), and distributed state synchronization via CRDTs and Durable Objects.

15 min read•3 Quiz Questions
#279Multi-Cloud Infrastructure, Hybrid Mesh & Kernel Observability with eBPFAdvanced PRO

Architecting resilient multi-cloud and hybrid cloud topologies: overcoming data gravity and egress costs, asynchronous CDC data replication, unified Kubernetes control planes, and sidecarless kernel-bypass networking/observability via eBPF and Cilium.

16 min read•3 Quiz Questions
#280Compliance, Immutable Audit Trails & Post-Quantum Cryptographic SystemsAdvanced PRO

Architecting immutable financial ledgers, Merkle tree anchoring, WORM storage compliance (SEC Rule 17a-4), crypto-shredding for GDPR Right-to-Erasure, Web3 content-addressed storage (IPFS/Merkle DAGs), and transitioning to NIST Post-Quantum Cryptography (ML-KEM/Kyber, ML-DSA/Dilithium).

16 min read•3 Quiz Questions