ecs.e3.7xlarge is a ecs.e3 family (32 vCPUs, 256 GiB RAM, 0 GB storage) server offered by Alibaba Cloud with 32 vCPUs, 256 GiB of memory and 0 GB of storage. The pricing starts at 0.5643 USD per hour.
ecs.e3 family (32 vCPUs, 256 GiB RAM, 0 GB storage)
Family
ecs.e3
Hw Virt
-
Status
active
Observed At
2026-07-12T09:26:02.260560
Availability
REGION / ID
SPOT
ONDEMAND
Shanghai (CN) / cn-shanghai
0.5643 USD/h
4.031 USD/h
Beijing (CN) / cn-beijing
0.7256 USD/h
4.031 USD/h
Hangzhou (CN) / cn-hangzhou
-
4.031 USD/h
Shenzhen (CN) / cn-shenzhen
-
4.031 USD/h
Processor
vCPUs
32
Hypervisor
KVM
CPU Allocation
Dedicated
CPU Cores
32
CPU Architecture
x86_64
System Resources and Accelerators
MEMORY
Memory Amount
256 GiB
GPU
GPU Count
0
GPU Memory Min
0 MiB
GPU Memory Total
0 MiB
GPUs
STORAGE
Storage Size
0 GB
Storages
NETWORK
Inbound Traffic
0 GB/month
Outbound Traffic
0 GB/month
IPv4
0
Server Description
A memory-optimized virtual server featuring dedicated x86_64 compute cores and a high memory-to-CPU ratio for demanding enterprise workloads.
Memory Optimized
Alibaba Cloud ecs.e3.7xlarge is a memory-optimized virtual machine instance from the ecs.e3 family, built on the x86_64 architecture and managed via the KVM hypervisor. It features 32 dedicated physical cores and 32 vCPUs with a single thread per core configuration, ensuring dedicated compute allocation. The instance is equipped with 256.0 GB of RAM, yielding a high memory-to-core ratio of 8.0 GB per core. It does not include local storage or GPU accelerators, relying entirely on network-attached storage. This hardware profile is designed for memory-intensive workloads, such as in-memory databases, large-scale caching, and enterprise applications, offering a stable and predictable resource environment.
Economics
Average Price per Region
Prices per Zone
Lowest Prices
Workload Profiles
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.
Precomputed compound score for CI/CD Build 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% Geekbench Clang compilation (multi-core), 10% Geekbench Clang compilation (single-core), 20% stress-ng div16 best-N cores, 5% PassMark integer math, 5% PassMark compression, 5% Brotli compression (multi-core, level 0), 5% PassMark string sorting. Rationale for component selection: Build performance is mainly driven by multi-core compilation throughput, but also bundles single-core compilation speed and general CPU performance, multi-core compression and text/scripting processing.
Precomputed compound score for Compute Heavy Applications 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: 15% stress-ng div16 best-N cores, 10% stress-ng div16 single core, 20% PassMark CPU Mark (composite), 10% Memory bandwidth (read, 64 MB), 15% PassMark floating point, 15% PassMark AVX/SSE/FMA (SIMD), 10% PassMark integer math, 5% PassMark physics simulation. Rationale for component selection: Number-crunching workload augmenting raw CPU performance stressing, general CPU performance benchmarks, memory bandwidth, and pure math computation speed like floating point, integer, SIMD (AVX/SSE/FMA) operations.
Precomputed compound score for Data Analysis 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: 70% PassMark CPU Mark (composite), 10% Gzip compression (single-core, level 5), 10% Memory bandwidth (read, 64 MB), 10% PassMark Memory Mark (composite). Rationale for component selection: Data analysis and ETL workloads are memory-bandwidth-bound and CPU-throughput-driven. The profile combines general CPU performance and memory bandwidth/latency as the primary drivers, supplemented by single-core compression speed as a proxy for serialisation-heavy ETL tasks.
Precomputed compound score for LLM Inference 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: 15% LLM text generation (SmolLM-135M, 128 tok), 15% LLM prompt processing (SmolLM-135M, 512 tok), 15% LLM text generation (Llama 7B, 128 tok), 15% LLM prompt processing (Llama 7B, 512 tok), 15% LLM text generation (Llama-3.3 70B, 128 tok), 15% LLM prompt processing (Llama-3.3 70B, 512 tok), 5% Memory bandwidth (read, 256 MB), 2% PassMark AVX/SSE/FMA (SIMD), 2% PassMark floating point. Rationale for component selection: VRAM and memory-bandwidth-bound LLM inference workload, using direct LLM speed benchmarks at three model sizes, and supplementing with raw memory bandwidth and SIMD performance benchmarks.
Precomputed compound score for Web Server 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: 30% Static web RPS (1 KiB, 8 conn/vCPU), 20% Static web RPS (64 KiB, 8 conn/vCPU), 20% Static web throughput (256 KiB, 8 conn/vCPU), 20% OpenSSL AES-256-CBC (16 kB blocks), 5% Gzip compression (multi-core, level 5), 5% PassMark string sorting. Rationale for component selection: Primary workloads drivers are single-process static HTTP serving speed and throughput, text processing, TLS termination, and asset compression.
ecs.e3.7xlarge is a ecs.e3 family (32 vCPUs, 256 GiB RAM, 0 GB storage) server offered by Alibaba Cloud with 32 vCPUs, 256 GiB of memory and 0 GB of storage. The pricing starts at 0.5643 USD per hour.
The ecs.e3.7xlarge server is equipped with 32 logical CPU cores on 32 physical CPU cores, 256 GiB of memory, 0 GB of storage, and no GPU. Additional block storage can be attached as needed.
The pricing for ecs.e3.7xlarge servers starts at 0.5643 USD per hour, but the actual price depends on the selected region, zone and server allocation method (e.g. on-demand versus spot pricing options): currently, we track the prices in 12 regions and zones every 5 minutes, and the maximum price stands at 4.031 USD.
The ecs.e3.7xlarge server is offered by Alibaba Cloud, founded in 2009, headquartered in Zhejiang, China. For more information, visit the Alibaba Cloud homepage.
The ecs.e3.7xlarge server is available in 12 availability zones of the following 4 regions: Shanghai (CN), Beijing (CN), Hangzhou (CN), Shenzhen (CN).
