ecs.c8a.12xlarge by Alibaba Cloud

ecs.c8a.12xlarge is a ecs.c8a family (48 vCPUs, 96 GiB RAM, 0 GB storage) server offered by Alibaba Cloud with 48 vCPUs, 96 GiB of memory and 0 GB of storage. The pricing starts at 1.4385 USD per hour.
48 vCPU
96 GiB Memory
Spare SCore
73,776
(All-cores)
3,072
(Single-core)

Specifications

Server Metadata

Vendor ID
alicloud
Name
ecs.c8a.12xlarge
Description
ecs.c8a family (48 vCPUs, 96 GiB RAM, 0 GB storage)
Family
ecs.c8a
Hw Virt
Average Time To Start
13
Status
active
Observed At
2026-09-13T22:49:41.277512

Availability

REGION / IDSPOTONDEMAND
Shenzhen (CN) / cn-shenzhen
- 1.4385 USD/h

Processor

vCPUs
48
Hypervisor
KVM
CPU Allocation
Dedicated
CPU Cores
48
CPU Speed
2.7 GHz
CPU Architecture
x86_64
CPU Manufacturer
AMD
CPU Family
EPYC
CPU Model
9T24
CPU L1D Cache
32 KiB
CPU L1D Cache Total
768 KiB
CPU L1I Cache
32 KiB
CPU L1I Cache Total
768 KiB
CPU L2 Cache
1 MiB
CPU L2 Cache Total
24 MiB
CPU L3 Cache
32 MiB
CPU L3 Cache Total
96 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, amd_lbr_v2, 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, extapic, cr8_legacy, abm, sse4a, misalignsse, 3dnowprefetch, osvw, ibs, topoext, perfctr_core, perfctr_llc, mwaitx, ssbd, perfmon_v2, ibrs, ibpb, stibp, ibrs_enhanced, vmmcall, 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, avx512_bf16, clzero, irperf, xsaveerptr, rdpru, wbnoinvd, arat, avx512vbmi, umip, pku, ospke, avx512_vbmi2, gfni, vaes, vpclmulqdq, avx512_vnni, avx512_bitalg, avx512_vpopcntdq, rdpid, fsrm
Ecpus
24
Scalability
50

System Resources and Accelerators

MEMORY
Memory Amount
96 GiB
Memory Amount Actual
96 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
      16 Gbps
      Inbound Traffic
      0 GB/month
      Outbound Traffic
      0 GB/month
      IPv4
      0

      CPU and System Topology

      Server Description

      A compute-optimized x86_64 server featuring 48 dedicated AMD EPYC cores and a 1:2 vCPU-to-RAM ratio for intensive processing workloads.

      Compute Optimized

      Alibaba Cloud ecs.c8a.12xlarge is an x86_64 compute-optimized server featuring 48 dedicated AMD EPYC 9T24 vCPUs running at 2.7 GHz and 96.0 GB of RAM. Operating on a KVM hypervisor, this instance lacks local storage and GPU accelerators but provides a baseline network bandwidth of 16 Gbps. Benchmark data shows strong CPU performance, with stress-ng and Geekbench multi-core scores in the top 25%. The processor supports AVX-512 and SHA_NI instructions, enabling efficient cryptography and vector processing. While cached memory reads are highly performant, uncached large-block memory bandwidth remains average. This resource profile makes the instance cost-effective for CPU-intensive workloads that do not require massive memory allocations, such as software compilation, multi-threaded decompression, and database operations.

      Economics

      Average Price per Region

      Prices per Zone

      Lowest Prices

      Performance

      Workload Profiles

      1.97Score
      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) 4,380,528 ops/sec 1,917,397 ops/sec 2.28 50.00% 50.00% +51.00%
      Redis RPS (pipeline=16, SET) 33,935,587 ops/sec 11,681,410 ops/sec 2.91 20.00% 20.00% +23.80%
      PassMark Memory Mark (composite) 2,826 2,418 1.17 10.00% 10.00% +1.58%
      Memory bandwidth (read, 16 MB ~ L3) 126,399 MB/sec 108,140 MB/sec 1.17 10.00% 10.00% +1.58%
      PassMark single-thread CPU 2,871 Mops/s 2,365 Mops/s 1.21 10.00% 10.00% +1.92%

      Memory Bandwidth

      Compression

      OpenSSL

      Geekbench Single-Core

      Score: 2,008

      Geekbench Multi-Core

      Score: 18,295

      Passmark CPU Scores

      BENCHMARKSCORE
      Mark
      62854
      Compression
      925111
      Encryption
      57770
      Extended Instructions
      64824
      Floating Point Maths
      127522
      Integer Maths
      228001
      Physics
      7190
      Prime Numbers
      365
      Single Threaded
      2871
      String Sorting
      134602

      Passmark Memory Scores

      BENCHMARKSCORE
      Memory Mark
      2826
      Database Operations
      22342
      Memory Latency
      75
      Memory Read Cached
      27343
      Memory Read Uncached
      26149
      Memory Write
      25953

      Stress-ng Raw Scores

      Stress-ng Relative Multicore Performance

      Static Web Server

      Redis

      Alternatives

      Servers of the Same Family

      INSTANCEvCPUsMEMORYGPUs
      ecs.c8a.large 24 GiB0
      ecs.c8a.xlarge 48 GiB0
      ecs.c8a.2xlarge 816 GiB0
      ecs.c8a.4xlarge 1632 GiB0
      ecs.c8a.8xlarge 3264 GiB0
      ecs.c8a.16xlarge 64128 GiB0
      ecs.c8a.24xlarge 96192 GiB0

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      ecs.c8a.12xlarge FAQs