ecs.ebmgn7i.32xlarge is a ecs.ebmgn7i family (128 vCPUs, 768 GiB RAM, 0 GB storage, 4xA10 24576 GiB VRAM) server offered by Alibaba Cloud with 128 vCPUs, 768 GiB of memory and 0 GB of storage. The pricing starts at 8.2404 USD per hour.
A bare-metal instance featuring dedicated physical cores and quad-GPU acceleration for parallel computing and deep learning workloads.
GPU AcceleratedCompute Optimized
Alibaba Cloud ecs.ebmgn7i.32xlarge is an x86_64 bare-metal instance featuring 128 dedicated physical cores and 128 vCPUs with a single thread per core design. It is equipped with 768.0 GB of system memory and four NVIDIA A10 GPUs providing a total of 96 GB of VRAM. Operating on the KVM hypervisor, this instance offers a baseline network bandwidth of 64 Gbps but does not include local storage. The dedicated CPU allocation and multi-GPU configuration make it suitable for parallel computing tasks, deep learning inference, and complex simulations where hyperthreading overhead must be avoided. It provides a dense hardware profile optimized for compute-intensive and graphics-accelerated enterprise workloads.
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.
Alternatives
Servers of the Same Family
INSTANCE
vCPUs
MEMORY
GPUs
Similar Servers
INSTANCE
VENDOR
vCPUs
MEMORY
GPUs
ecs.ebmgn7i.32xlarge FAQs
ecs.ebmgn7i.32xlarge is a ecs.ebmgn7i family (128 vCPUs, 768 GiB RAM, 0 GB storage, 4xA10 24576 GiB VRAM) server offered by Alibaba Cloud with 128 vCPUs, 768 GiB of memory and 0 GB of storage. The pricing starts at 8.2404 USD per hour.
The ecs.ebmgn7i.32xlarge server is equipped with 128 logical CPU cores on 128 physical CPU cores, 768 GiB of memory, 0 GB of storage, and 4 A10 GPUs. Additional block storage can be attached as needed.
The pricing for ecs.ebmgn7i.32xlarge servers starts at 8.2404 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 1 regions and zones every 5 minutes, and the maximum price stands at 8.2404 USD.
The ecs.ebmgn7i.32xlarge server is offered by Alibaba Cloud, founded in 2009, headquartered in Zhejiang, China. For more information, visit the Alibaba Cloud homepage.
The ecs.ebmgn7i.32xlarge server is available in 1 availability zones of the following 1 regions: Beijing (CN).
