0x00 实验环境
为了保证测试的严谨性，本次对比测试的具体环境配置如下：
测试软件：Hashcat v6.2.3
对比显卡：RTX 5060 (8GB) / Tesla P100 (16GB)
测试字典：文件大小 9.8GB，共计 9.4 亿条记录
0x01 运行结果
# P100 Hashcat 输出
Session..........: hashcat
Status...........: Exhausted
Hash.Name........: WPA-EAPOL-PBKDF2
Hash.Target......: xxx
Time.Started.....: Fri Jan 22 12:13:51 2021 (45 mins, 44 secs)
Time.Estimated...: Fri Jan 22 12:59:35 2021 (0 secs)
Guess.Base.......: File (dict.dic)
Guess.Queue......: 1/1 (100.00%)
Speed.#1.........: 343.6 kH/s (3.00ms) @ Accel:8 Loops:32 Thr:1024 Vec:1
Recovered........: 0/1 (0.00%) Digests
Progress.........: 942424852/942424852 (100.00%)
Rejected.........: 0/942424852 (0.00%)
Restore.Point....: 942424852/942424852 (100.00%)
Restore.Sub.#1...: Salt:0 Amplifier:0-1 Iteration:0-1
Candidates.#1....: [email protected] -> ZZZZZZZZZZZZZZZZ
Hardware.Mon.#1..: Temp: 72c Util: 49% Core:1328MHz Mem: 715MHz Bus:16
# 5060 Hashcat 输出
Session..........: hashcat
Status...........: Exhausted
Hash.Name........: WPA-EAPOL-PBKDF2
Hash.Target......: xxx
Time.Started.....: Wed Aug 5 23:50:53 2026 (31 mins, 24 secs)
Time.Estimated...: Thu Aug 6 00:22:17 2026 (0 secs)
Kernel.Feature...: Pure Kernel
Guess.Base.......: File (dict.dic)
Guess.Queue......: 1/1 (100.00%)
Speed.#1.........: 500.7 kH/s (5.34ms) @ Accel:8 Loops:64 Thr:1024 Vec:1
Recovered........: 0/1 (0.00%) Digests
Progress.........: 942424852/942424852 (100.00%)
Rejected.........: 0/942424852 (0.00%)
Restore.Point....: 942424852/942424852 (100.00%)
Restore.Sub.#1...: Salt:0 Amplifier:0-1 Iteration:0-1
Candidate.Engine.: Device Generator
Candidates.#1....: zzq221000 -> ZZZZZZZZZZZZZZZZ
Hardware.Mon.#1..: Temp: 68c Fan: 73% Util: 97% Core:2827MHz Mem:13801MHz Bus:8
0x03 结果对比
从实测数据来看，RTX 5060展现出了惊人的性能跃升：其运行速度较上一代经典专业计算卡P100大幅提升了45.7%。在处理9.8GB字典数据的测试场景中，5060更是将耗时缩短了整整14分钟，效率提升肉眼可见。
回望2021至2026这五年，一张定位“甜品级”的消费级显卡，如今已能在实际运算中全面超越昔日的专业计算卡。这背后，不仅是GPU制造工艺的代际飞跃，更是NVIDIA在CUDA生态与底层架构上多年深耕、持续迭代的必然结果。消费级算力正在以惊人的速度重塑行业格局。
下一期，我将把目光转向更高阶的RTX 4070 Ti，看看它在同类场景下又能带来怎样的性能表现。测试数据即将出炉，大家敬请期待！