ecs.g7a.4xlarge by Alibaba Cloud

ecs.g7a.4xlarge is a ecs.g7a family (16 vCPUs, 64 GiB RAM, 0 GB storage) server offered by Alibaba Cloud with 16 vCPUs, 64 GiB of memory and 0 GB of storage. The pricing starts at 0.1036 USD per hour.
16 vCPU
64 GiB Memory
Spare SCore
21,587
(All-cores)
2,677
(Single-core)

Specifications

Server Metadata

Vendor ID
alicloud
Name
ecs.g7a.4xlarge
Description
ecs.g7a family (16 vCPUs, 64 GiB RAM, 0 GB storage)
Family
ecs.g7a
Hw Virt
Status
active
Observed At
2026-11-02T04:15:57.157511

Availability

REGION / IDSPOTONDEMAND
Ulanqab (CN) / cn-wulanchabu
- 0.49 USD/h
Guangzhou (CN) / cn-guangzhou
0.11175 USD/h 0.545 USD/h
Beijing (CN) / cn-beijing
- 0.545 USD/h
Chengdu (CN) / cn-chengdu
- 0.545 USD/h
Hangzhou (CN) / cn-hangzhou
- 0.545 USD/h
Shanghai (CN) / cn-shanghai
- 0.545 USD/h
Shenzhen (CN) / cn-shenzhen
- 0.545 USD/h
Virginia (US) / us-east-1
0.1613 USD/h 0.6204 USD/h
Frankfurt (DE) / eu-central-1
0.172 USD/h 0.782 USD/h
Jakarta (ID) / ap-southeast-5
0.1764 USD/h 0.802 USD/h
Tokyo (JP) / ap-northeast-1
0.19585 USD/h 0.816 USD/h
Singapore (SG) / ap-southeast-1
0.2122 USD/h 0.816 USD/h
Hong Kong (HK) / cn-hongkong
- 0.816 USD/h

Processor

vCPUs
16
Hypervisor
KVM
CPU Allocation
Dedicated
CPU Cores
8
CPU Speed
2.55 GHz
CPU Architecture
x86_64
CPU Manufacturer
AMD
CPU Family
EPYC
CPU Model
7T83
CPU L1D Cache
32 KiB
CPU L1D Cache Total
256 KiB
CPU L1I Cache
32 KiB
CPU L1I Cache Total
256 KiB
CPU L2 Cache
512 KiB
CPU L2 Cache Total
4 MiB
CPU L3 Cache
32 MiB
CPU L3 Cache Total
32 MiB
CPU Flags
fpu, vme, de, pse, tsc, msr, pae, mce, cx8, apic, sep, mtrr, pge, mca, cmov, pat, pse36, clflush, mmx, fxsr, sse, sse2, ht, syscall, nx, mmxext, fxsr_opt, pdpe1gb, rdtscp, lm, constant_tsc, rep_good, nopl, nonstop_tsc, cpuid, extd_apicid, aperfmperf, tsc_known_freq, pni, pclmulqdq, monitor, ssse3, fma, cx16, pcid, sse4_1, sse4_2, x2apic, movbe, popcnt, aes, xsave, avx, f16c, rdrand, hypervisor, lahf_lm, cmp_legacy, cr8_legacy, abm, sse4a, misalignsse, 3dnowprefetch, osvw, topoext, ssbd, ibrs, ibpb, stibp, vmmcall, tsc_adjust, bmi1, avx2, smep, bmi2, erms, invpcid, rdseed, adx, smap, clflushopt, clwb, sha_ni, xsaveopt, xsavec, xgetbv1, xsaves, clzero, xsaveerptr, wbnoinvd, arat, vaes, vpclmulqdq, rdpid, fsrm
Ecpus
8.1
Scalability
50.62

System Resources and Accelerators

MEMORY
Memory Amount
64 GiB
Memory Amount Actual
64 GiB
ACCELERATORS
Accelerator Count
0
Accelerator Memory Min
0 MiB
Accelerator Memory Total
0 MiB
Accelerators
    STORAGE
    Storage Size
    0 GB
    Storages
      NETWORK
      Network Speed Baseline
      7 Gbps
      Inbound Traffic
      0 GB/month
      Outbound Traffic
      0 GB/month
      IPv4
      0

      CPU and System Topology

      Server Description

      A balanced general-purpose virtual server featuring dedicated AMD EPYC processors and 64 GB of memory for reliable enterprise workloads.

      General Purpose

      Alibaba Cloud ecs.g7a.4xlarge is a general-purpose instance featuring 16 dedicated vCPUs and 64.0 GB of RAM, powered by an AMD EPYC 7T83 processor running at 2.55 GHz on the KVM hypervisor. The x86_64 architecture includes 8 physical cores with hyper-threading and a 32 MB L3 cache. This instance does not bundle local storage or GPUs, relying on network-attached storage and offering a baseline network bandwidth of 7 Gbps. Performance benchmarks show strong single-core CPU execution and small-model text generation, alongside average multi-core, memory read, and database operation speeds, though large-block memory write bandwidth is weak. The balanced vCPU-to-RAM ratio makes it a cost-efficient choice for general-purpose workloads, including web serving, application hosting, software compilation, and moderate database operations.

      Economics

      Average Price per Region

      Prices per Zone

      Lowest Prices

      Performance

      Workload Profiles

      0.69Score
      Precomputed compound score for Cache Intensive workloads. A weighted average (geometric mean) of benchmark scores compared to their medians: score = ∏ (x_i / m_i)^(w_i / Σw). The score of 1.0 represents a synthetic baseline server with the median performance of each component benchmark; 0.5 means roughly half the performance; and 2.0 means twice the performance of that reference profile. Component weights: 50% Redis RPS (pipeline=1, SET), 20% Redis RPS (pipeline=16, SET), 10% PassMark Memory Mark (composite), 10% Memory bandwidth (read, 16 MB ~ L3), 10% PassMark single-thread CPU. Rationale for component selection: In-memory key-value store workload, mixing direct Redis performance metrics with memory speed and latency benchmarks, and single-core CPU performance profiles.
      Component Score Weight Impact
      Raw Reference Normalized Target Actual
      Redis RPS (pipeline=1, SET) 1,182,880 ops/sec 1,918,343 ops/sec 0.63 50.00% 50.00% -21.00%
      Redis RPS (pipeline=16, SET) 8,890,187 ops/sec 11,704,734 ops/sec 0.74 20.00% 20.00% -5.80%
      PassMark Memory Mark (composite) 2,754 2,416 1.2 10.00% 10.00% +1.80%
      Memory bandwidth (read, 16 MB ~ L3) 42,209 MB/sec 108,466 MB/sec 0.38 10.00% 10.00% -9.20%
      PassMark single-thread CPU 2,378 Mops/s 2,365 Mops/s 1 10.00% 10.00% 0.00%

      Memory Bandwidth

      Compression

      OpenSSL

      Geekbench Single-Core

      Score: 1,536

      Geekbench Multi-Core

      Score: 8,861

      Passmark CPU Scores

      BENCHMARKSCORE
      Mark
      		 21,236
      Compression
      		 250,866
      Encryption
      		 17,105
      Extended Instructions
      		 14,915
      Floating Point Maths
      		 36,385
      Integer Maths
      		 66,477
      Physics
      		 1,686
      Prime Numbers
      		 93
      Single Threaded
      		 2,378
      String Sorting
      		 35,058

      Passmark Memory Scores

      BENCHMARKSCORE
      Memory Mark
      		 2,754
      Database Operations
      		 6,741
      Memory Latency
      		 55
      Memory Read Cached
      		 23,569
      Memory Read Uncached
      		 17,984
      Memory Write
      		 15,732

      Stress-ng Stressors

      CPU integerSingleMultiScaling
      atomic
      		 239.84 205.48 0.86×
      bitops
      		 141,239 1,307,187 9.26×
      branch
      		 164,299 2,416,379 14.71×
      funccall
      		 539.89 5,579 10.33×
      funcret
      		 17,114 178,235 10.41×
      hash
      		 136,800 1,670,077 12.21×
      intmath
      		 58,442 633,002 10.83×
      nop
      		 282,133 2,283,220 8.09×
      prime
      		 393,421 3,876,899 9.85×
      rotate
      		 77,204 618,701 8.01×
      str
      		 862,821 8,458,527 9.80×
      wcs
      		 566,271 5,188,654 9.16×
      CryptographySingleMultiScaling
      crypt
      		 3,198 25,414 7.95×
      CompressionSingleMultiScaling
      jpeg
      		 724.39 7,953 10.98×
      zlib
      		 58.83 745.56 12.67×
      HypervisorSingleMultiScaling
      clock
      		 23,026 234,705 10.19×
      clone
      		 395.17 2,484 6.29×
      context
      		 3,159 42,309 13.39×
      cpu-sched
      		 67.17 1,027 15.29×
      eventfd
      		 63,881 798,628 12.50×
      fault
      		 7,881 102,664 13.03×
      fork
      		 4,145 42,129 10.16×
      getrandom
      		 45,240 459,018 10.15×
      hrtimers
      		 134,869 1,776,110 13.17×
      mprotect
      		 1,125,390 2,673,379 2.38×
      mtx
      		 29,753,827 876,397,173 29.45×
      munmap
      		 2,751 29,981 10.90×
      null
      		 405,681 5,197,931 12.81×
      pipe
      		 1,491,594 5,140,459 3.45×
      priv-instr
      		 409,662 5,305,137 12.95×
      resched
      		 4,474,989 1,306,848 0.29×
      switch
      		 130,621 1,617,584 12.38×
      timermix
      		 247,498 2,998,622 12.12×
      tlb-shootdown
      		 19,629 53,907 2.75×
      tsc
      		 669,323 10,299,477 15.39×
      usersyscall
      		 295,021 3,780,371 12.81×
      vdso
      		 76,691,674 1,043,955,204 13.61×
      vfork
      		 29,492 200,818 6.81×
      x86syscall
      		 4,874,377 62,265,053 12.77×
      yield
      		 8,131,059 8,726,959 1.07×
      zero
      		 2,792,081 33,406,546 11.96×
      CPU float/SIMDSingleMultiScaling
      eigen
      		 13,555 115,250 8.50×
      expmath
      		 2,107 28,140 13.35×
      fma
      		 780,811 9,870,007 12.64×
      fp
      		 227.82 2,636 11.57×
      fractal
      		 6.8 89.41 13.15×
      hyperbolic
      		 3,291 40,080 12.18×
      logmath
      		 3,874 44,468 11.48×
      matrix
      		 2,712 32,169 11.86×
      matrix-3d
      		 185.14 1,339 7.23×
      monte-carlo
      		 182.51 2,240 12.28×
      mpfr
      		 1,392 13,669 9.82×
      powmath
      		 1,182 15,541 13.15×
      trig
      		 1,493 18,482 12.38×
      veccmp
      		 15,981 168,678 10.55×
      vecfp
      		 1,479 16,261 11.00×
      vecmath
      		 3,419 44,191 12.92×
      vecshuf
      		 533.96 4,976 9.32×
      vecwide
      		 23,656 241,102 10.19×
      AlgorithmsSingleMultiScaling
      bitonicsort
      		 8.6 73.6 8.56×
      bsearch
      		 261.95 2,795 10.67×
      bubblesort
      		 0.8 9.6 12.00×
      fibsearch
      		 320.74 3,080 9.60×
      heapsort
      		 9.4 91.6 9.74×
      hsearch
      		 7,590 69,381 9.14×
      insertionsort
      		 11.6 92.8 8.00×
      malloc
      		 3,054,810 35,907,670 11.75×
      mcontend
      		 238.45 3,064 12.85×
      mergesort
      		 28 321.99 11.50×
      qsort
      		 18.58 202.79 10.91×
      skiplist
      		 6,191 75,879 12.26×
      tree
      		 1.6 11.79 7.37×
      MemorySingleMultiScaling
      cache
      		 661,367 1,425,391 2.16×
      cachehammer
      		 307,497 3,948,876 12.84×
      cacheline
      		 33.98 598.04 17.60×
      l1cache
      		 146.87 1,260 8.58×
      memcpy
      		 366.01 2,801 7.65×
      memrate
      		 2.55 5.53 2.17×
      memthrash
      		 118.62 362.05 3.05×
      ptr-chase
      		 40,970,859 124,477,029 3.04×
      spinmem
      		 365.79 1,220 3.33×
      stream
      		 101.44 2,047 20.18×
      vm
      		 58,577 293,032 5.00×

      Stress-ng Raw Scores

      Stress-ng Relative Multicore Performance

      LLM Inference Speed for Prompt Processing

      LLM Inference Speed for Text Generation

      Static Web Server

      Redis

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      ecs.g7a.4xlarge FAQs