Do you STILL suck at subnetting?? (THE FINAL TEST) // EP 9

About this video

This final episode of the subnetting series tackles VLSM — variable length subnet masking — by carving one network into subnets of different sizes. The challenge is to take 172.21.42.0/24 and build four factory networks for Bernard Hackwell of Hackwell Industries: guest, robots, servers and workers, each with different host requirements. Chuck’s method is to work from the largest requirement to the smallest and to start each new subnet where the previous one ended. Workers come first with 117 hosts, needing seven host bits, which produces a /25 mask and an increment of 128, covering addresses 0 through 127. Robots follow with 57 hosts, producing a /26 mask of 255.255.255.192 and an increment of 64, covering 128 through 191. Servers, with 26 hosts, get a /27 mask of 255.255.255.224 and an increment of 32, covering 192 through 223, and the guest network is then placed at 224 as a /28 ending at 239. The lesson is that VLSM is not a new technique: it is the same host-based subnetting process learned earlier, repeated four times in size order. He closes by warning that the skill fades without practice and pointing viewers to practice exams and labs.

Key takeaways

• VLSM means subnetting a subnet into pieces of different sizes, using different masks taken from a single original network
• Always allocate from the largest host requirement to the smallest, and begin each subnet where the previous subnet ended
• Workers with 117 hosts took a /25 with an increment of 128, using 172.21.42.0 through 172.21.42.127 from the /24
• Robots with 57 hosts took a /26 and increment of 64 (128-191); servers with 26 hosts took a /27 and increment of 32 (192-223); guest followed as a /28 at 224-239
• VLSM repeats the same host-based subnetting steps already learned, just in big-to-small order, and the skill needs regular practice to retain

Episode details: published August 23, 2023 · 7:51 long. Summary written by Self Growth Videos from the episode itself. Business results and any income figures mentioned are NetworkChuck’s own claims, shared for education — they are not typical or guaranteed.

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