EC2 Instance Types — Family Codes & Meanings
The 5 EC2 categories (mental models)
Section titled “The 5 EC2 categories (mental models)”| Type | Meaning | Use for | Families |
|---|---|---|---|
| General | Balanced CPU/mem/net | Web servers, dev, small DB | t3, m5, m7g |
| Compute Optimized | CPU-intensive | Transcoding, batch, gaming, ML inference | c5, c7g |
| Memory Optimized | RAM-intensive | In-memory caches (Redis), in-memory DB, SAP HANA | r5, x2gd, u-*, z1d |
| Accelerated | GPU/FPGA/AI chips | ML training, rendering, simulation | p4, g5, inf2, trn1 |
| Storage Optimized | I/O-bound, FAST disk (not just big) | NoSQL (Cassandra/Mongo), data warehouse, ElasticSearch | i3, d2, h1 |
Storage Optimized ≠ “more storage” — it’s low-latency high-I/O (NVMe SSD), for random read/write heavy workloads.
Family code letters
Section titled “Family code letters”[family][generation][g?].[size] e.g. c5.large, m7g.xlarge, t3.micro
| Letter | Meaning |
|---|---|
| t | Burstable (CPU credits) — cheap, low avg load |
| m | General purpose |
| c | Compute |
| r | Memory (RAM) |
| x | Extra-large memory |
| z | High-frequency (high clock speed) |
| u | Ultra-high memory (up to 24TB) |
| p | GPU (Tesla) |
| g | GPU (graphics) |
| inf | Inferentia (ML inference chip) |
| trn | Trainium (ML training chip) |
| i | Storage / I/O optimized |
| d | Dense storage (HDD, throughput) |
| h | High disk throughput |
| f | FPGA |
| a | ARM (first-gen Graviton) |
| mac | Mac instances |
Generation + Graviton
Section titled “Generation + Graviton”- Number = generation (5 = 5th gen)
gsuffix = Graviton (AWS ARM chip): m5 → m6g → m7g. ~20% better price/performance- No
g= x86 (Intel/AMD)
nano → micro → small → medium → large → xlarge → 2xlarge → ... → 16xlarge → 24xlarge → metal
- Each step ≈ doubles vCPU/RAM
metal= bare metal (no virtualization)- Free tier =
t3.micro(1 vCPU, 1GB RAM, 750h/mo first 12 months)
Example decode
Section titled “Example decode”t3.micro → t (burstable) gen3, micro sizec7g.2xlarge → c (compute) gen7 + g (Graviton ARM), 2xlarger5.large → r (memory) gen5, largep4d.24xlarge → p (GPU) gen4, 24xlargeExam relevance
Section titled “Exam relevance”t= burstable cheap,c= compute,r= memory,m= generalgsuffix = Graviton/ARM (cheaper)- Scenario → match type (compute-intensive → c, RAM-heavy → r, GPU → p/g)
Related
Section titled “Related”- [[aws/index]]
- [[aws/compute-map/index]]