GPU cluster

Unleash Infinite AI Power

NVIDIA RTX 5090 / A100 / H100 / H200 GPU clusters — 2,000+ GPUs on InfiniBand, powering LLM training, inference acceleration and HPC.

2000+
GPU
479+
PFLOPS (mixed precision)
4
GPU Models

Flagship GPU Cluster Configurations

Multiple GPU models and cluster sizes for every stage of AI

NVIDIA RTX 5090
GPU ModelRTX 5090 32GB GDDR7
Cluster Scale1,024 GPUs
InterconnectPCIe 5.0 + InfiniBand HDR
Total VRAM32 TB
Peak Compute
287
PFLOPS (FP16)
Use cases:AI training, inference acceleration, video rendering
NVIDIA A100
GPU ModelA100 80GB SXM4
Cluster Scale512 GPUs
InterconnectNVLink + InfiniBand HDR
Total VRAM40 TB HBM2e
Peak Compute
80
PFLOPS (FP16)
Use cases:LLM training, scientific computing
NVIDIA H100
GPU ModelH100 80GB SXM5
Cluster Scale256 GPUs
InterconnectNVLink4 + InfiniBand NDR
Total VRAM20 TB HBM3
Peak Compute
51
PFLOPS (FP8)
Use cases:Trillion-parameter training, generative AI
NVIDIA H200
GPU ModelH200 141GB SXM5
Cluster Scale256 GPUs
InterconnectNVLink4 + InfiniBand NDR
Total VRAM36 TB HBM3e
Peak Compute
61
PFLOPS (FP8)
Use cases:Ultra-scale LLM training, long-sequence inference

AI Computing for Every Scenario

From 100B-parameter LLMs to real-time inference, from research computing to content creation

100B-Parameter LLM Training

A100/H100/H200 heterogeneous clusters with FP8/FP16 mixed-precision training. Supports Megatron-LM, DeepSpeed, Colossal-AI. > 85% scaling efficiency at 1,000 GPUs.

14 days
LLaMA2-70B continued pre-training
30 days+
100B-parameter model training
100B-Parameter LLM Training

Images for illustration only

High-Performance Inference

Deeply optimized with TensorRT-LLM, vLLM and Triton Inference Server. Continuous batching, KV-cache optimization and speculative sampling. < 100ms latency for 100B models.

2,000 tokens/s
LLaMA2-70B
500 tokens/s
100B-parameter models
High-Performance Inference

Images for illustration only

8K Cinematic Rendering

NVIDIA Omniverse + RTX 5090 / A100 clusters with distributed Blender, Maya and Houdini rendering. 8K frames: from hours to minutes.

30 sec
4K frame rendering
2 min
8K frame rendering
8K Cinematic Rendering

Images for illustration only

High-Performance Scientific Computing

Molecular dynamics, climate simulation, financial Monte Carlo. Full CUDA ecosystem: GROMACS, LAMMPS, OpenFOAM and more.

100M atoms
Molecular simulation
1M Monte Carlo sims/sec
Financial risk
High-Performance Scientific Computing

Images for illustration only

Industry-Leading Benchmarked Performance

Measured comparison between our clusters and same-spec public-cloud GPU instances under identical framework settings; industry averages from public benchmarks, for reference only

ResNet-50 trainingimages/s
SUOZHOU
3,850
Industry Average
2,850
BERT-Large pre-trainingseq/s
SUOZHOU
1,420
Industry Average
1,050
LLaMA2-7B trainingtokens/s
SUOZHOU
4,200
Industry Average
3,100
Stable Diffusionit/s
SUOZHOU
18
Industry Average
13
+35%
Training Efficiency Gain
85%+
1,000-GPU Scaling Efficiency
+40%
Inference Throughput Gain

Complete Software Stack

Pre-installed mainstream AI frameworks and toolchains, ready out of the box

P
PyTorch
Training Frameworks
T
TensorFlow
Training Frameworks
J
JAX
Training Frameworks
D
DeepSpeed
Distributed Training
M
Megatron-LM
LLM Training
v
vLLM
Inference Acceleration
T
TensorRT-LLM
Inference Optimization
N
NVIDIA NeMo
LLM Tooling
Container Orchestration
Kubernetes + Docker, second-level resource allocation
Image Marketplace
Pre-configured PyTorch/TensorFlow/CUDA images, one-click deploy
Distributed Storage
Parallel file system, 100GB/s+ training data loading
Monitoring & Alerts
Real-time GPU utilization, temperature and VRAM monitoring with automatic alerts

Customer Success Stories

From zero to large-scale clusters, SUOZHOU helped us complete LLaMA2-70B continued pre-training. Rock-solid stability — 30 days of large-scale training with zero interruption.

An LLM Startup40% lower training cost

Trailer rendering shrank from 2 weeks to 3 days. Elastic GPU scheduling let us scale fast at project peaks.

A Game Studio5x rendering efficiency

Monte Carlo simulation for risk models demands massive compute. SUOZHOU GPU clusters let us finish everything before trading hours.

A Fintech Company10x faster computing

Perception models need massive parallel training. SUOZHOU RTX 5090 clusters gave us a huge edge in iteration cycles — 60% higher training efficiency than the previous generation.

An Autonomous Driving Company60% faster model iteration

Get GPU Power in Three Steps

Step 1

Requirements Consultation

Talk to our solution architects about your model size, framework preferences and budget

1 business day
Step 2

Solution Design

We design the optimal cluster: GPU model, node count, network topology and storage

3 business days
Step 3

Cluster Handover

Software pre-installed, distributed training validated and accepted — ready to run

5 business days

Experience Top-Tier GPU Computing Now

AI startup, research institute or enterprise IT team — we tailor computing solutions for you