ecs.r7se.4xlarge by Alibaba Cloud

ecs.r7se.4xlarge is a ecs.r7se family (16 vCPUs, 128 GiB RAM, 0 GB storage) server offered by Alibaba Cloud with 16 vCPUs, 128 GiB of memory and 0 GB of storage. The pricing starts at 1.066 USD per hour.
16 vCPU
128 GiB Memory
Spare SCore
15,553
(All-cores)
1,843
(Single-core)

Specifications

Server Metadata

Vendor ID
alicloud
Name
ecs.r7se.4xlarge
Description
ecs.r7se family (16 vCPUs, 128 GiB RAM, 0 GB storage)
Family
ecs.r7se
Hw Virt
Status
active
Observed At
2026-10-06T14:31:55.410432

Availability

REGION / IDSPOTONDEMAND
Beijing (CN) / cn-beijing
- 1.066 USD/h
Hangzhou (CN) / cn-hangzhou
- 1.066 USD/h
Shanghai (CN) / cn-shanghai
- 1.066 USD/h
Shenzhen (CN) / cn-shenzhen
- 1.066 USD/h
Hong Kong (HK) / cn-hongkong
- 1.4146 USD/h

Processor

vCPUs
16
Hypervisor
KVM
CPU Allocation
Dedicated
CPU Cores
8
CPU Speed
2.9 GHz
CPU Architecture
x86_64
CPU Manufacturer
Intel
CPU Family
Xeon
CPU Model
8369B
CPU L1D Cache
48 KiB
CPU L1D Cache Total
384 KiB
CPU L1I Cache
32 KiB
CPU L1I Cache Total
256 KiB
CPU L2 Cache
1 MiB
CPU L2 Cache Total
10 MiB
CPU L3 Cache
48 MiB
CPU L3 Cache Total
48 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, ss, ht, syscall, nx, pdpe1gb, rdtscp, lm, constant_tsc, rep_good, nopl, xtopology, nonstop_tsc, cpuid, 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, abm, 3dnowprefetch, cpuid_fault, ibrs_enhanced, fsgsbase, tsc_adjust, bmi1, avx2, smep, bmi2, erms, invpcid, avx512f, avx512dq, rdseed, adx, smap, avx512ifma, clflushopt, clwb, avx512cd, sha_ni, avx512bw, avx512vl, xsaveopt, xsavec, xgetbv1, xsaves, wbnoinvd, arat, avx512vbmi, pku, ospke, avx512_vbmi2, gfni, vaes, vpclmulqdq, avx512_vnni, avx512_bitalg, avx512_vpopcntdq, rdpid, fsrm, arch_capabilities
Ecpus
8.4
Scalability
52.5

System Resources and Accelerators

MEMORY
Memory Amount
128 GiB
Memory Amount Actual
128 GiB
GPU
GPU Count
0
GPU Memory Min
0 MiB
GPU Memory Total
0 MiB
GPUs
    STORAGE
    Storage Size
    0 GB
    Storages
      NETWORK
      Network Speed Baseline
      6 Gbps
      Inbound Traffic
      0 GB/month
      Outbound Traffic
      0 GB/month
      IPv4
      0

      CPU and System Topology

      Server Description

      A memory-optimized x86_64 server featuring 128 GB RAM and dedicated Intel Xeon processors for memory-intensive caching and database workloads.

      Memory OptimizedGeneral Purpose

      Alibaba Cloud ecs.r7se.4xlarge is an x86_64 memory-optimized server configured with 16 dedicated vCPUs and 128.0 GB of RAM, powered by an Intel Xeon 8369B processor running at 2.9 GHz. It features a high memory-to-vCPU ratio of 8 GB per core but lacks local storage and GPU accelerators, relying on a 6 Gbps baseline network bandwidth. Performance benchmarks indicate average CPU capabilities, with a PassMark CPU score of 21,177.19, contrasted by a strong PassMark memory score in the top 25% tier. The server achieves 15.01 million Redis SET operations per second and 101.82 GB/sec large-block memory read bandwidth. Given its high memory density and lack of local storage, this instance is suited for in-memory databases, caching layers, and memory-bound data processing workloads.

      Economics

      Average Price per Region

      Prices per Zone

      Lowest Prices

      Performance

      Workload Profiles

      1.1Score
      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) 2,192,967 ops/sec 1,916,704 ops/sec 1.2 50.00% 50.00% +9.50%
      Redis RPS (pipeline=16, SET) 11,137,663 ops/sec 11,642,227 ops/sec 0.92 20.00% 20.00% -1.70%
      PassMark Memory Mark (composite) 2,767 2,417 1.2 10.00% 10.00% +1.80%
      Memory bandwidth (read, 16 MB ~ L3) 106,714 MB/sec 108,361 MB/sec 1 10.00% 10.00% 0.00%
      PassMark single-thread CPU 2,368 Mops/s 2,365 Mops/s 1 10.00% 10.00% 0.00%

      Memory Bandwidth

      Compression

      OpenSSL

      Geekbench Single-Core

      Score: 1,600

      Geekbench Multi-Core

      Score: 9,636

      Passmark CPU Scores

      BENCHMARKSCORE
      Mark
      21177
      Compression
      247724
      Encryption
      14676
      Extended Instructions
      14136
      Floating Point Maths
      34495
      Integer Maths
      67463
      Physics
      2284
      Prime Numbers
      135
      Single Threaded
      2368
      String Sorting
      35014

      Passmark Memory Scores

      BENCHMARKSCORE
      Memory Mark
      2767
      Database Operations
      8035
      Memory Latency
      52
      Memory Read Cached
      24296
      Memory Read Uncached
      12883
      Memory Write
      11741

      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

      Alternatives

      Servers of the Same Family

      INSTANCEvCPUsMEMORYGPUs
      ecs.r7se.large 216 GiB0
      ecs.r7se.xlarge 432 GiB0
      ecs.r7se.2xlarge 864 GiB0
      ecs.r7se.3xlarge 1296 GiB0
      ecs.r7se.6xlarge 24192 GiB0
      ecs.r7se.8xlarge 32256 GiB0
      ecs.r7se.16xlarge 64512 GiB0

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      ecs.r7se.4xlarge FAQs