ecs.gn5i-c16g1.4xlarge is a ecs.gn5i family (16 vCPUs, 64 GiB RAM, 0 GB storage, 1xP4 8192 GiB VRAM) server offered by Alibaba Cloud with 16 vCPUs, 64 GiB of memory and 0 GB of storage. The pricing starts at 0.4838 USD per hour.
A dedicated sixteen-core server featuring an integrated graphics processor and sixty-four gigabytes of memory for hardware-accelerated workloads.
GPU AcceleratedGeneral Purpose
Alibaba Cloud ecs.gn5i-c16g1.4xlarge is a KVM-based server instance featuring 16 dedicated x86_64 physical cores with a 1:1 thread-to-core ratio. It is configured with 64.0 GB of system memory, providing 4.0 GB per core. Acceleration is delivered via a single NVIDIA P4 GPU with 8 GB of VRAM. The instance does not feature local storage, requiring network-attached storage solutions, and operates with a baseline network bandwidth of 3 Gbps. This hardware profile offers a balanced resource density for workloads requiring dedicated CPU execution and entry-level GPU acceleration. It is suitable for tasks such as video transcoding, machine learning inference, and GPU-accelerated data processing.
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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.gn5i-c16g1.4xlarge is a ecs.gn5i family (16 vCPUs, 64 GiB RAM, 0 GB storage, 1xP4 8192 GiB VRAM) server offered by Alibaba Cloud with 16 vCPUs, 64 GiB of memory and 0 GB of storage. The pricing starts at 0.4838 USD per hour.
The ecs.gn5i-c16g1.4xlarge server is equipped with 16 logical CPU cores on 16 physical CPU cores, 64 GiB of memory, 0 GB of storage, and 1 P4 GPU. Additional block storage can be attached as needed.
The pricing for ecs.gn5i-c16g1.4xlarge servers starts at 0.4838 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 2.419 USD.
The ecs.gn5i-c16g1.4xlarge server is offered by Alibaba Cloud, founded in 2009, headquartered in Zhejiang, China. For more information, visit the Alibaba Cloud homepage.
The ecs.gn5i-c16g1.4xlarge server is available in 12 availability zones of the following 5 regions: Shenzhen (CN), Qingdao (CN), Beijing (CN), Hangzhou (CN), Shanghai (CN).
