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Computing Hardware
Computing hardware is the "machine tool" of the intelligent era
Determining the upper limit of computing performance and the baseline of system stability.
Magicsoft provides enterprises with high-performance GPU servers + distributed computing clusters as a one-stop hardware solution, fully meeting the needs of high-performance computing scenarios such as AI large models, Web3 blockchain, and big data analytics.
In the wave of digital transformation, computing power is no longer just a technical indicator, but a core manifestation of enterprise competitiveness. Whether training a hundred-billion-parameter large language model, running a high-concurrency blockchain network, or processing TB-level real-time data daily, all require powerful, stable, and scalable computing hardware as support. However, many enterprises face a dilemma in building computing power: purchasing public cloud computing resources leads to high long-term costs; building hardware in-house raises concerns about wrong technology selection, complex operations, and difficult scaling. Magicsoft identified this pain point and productized, solutionized, and servitized computing hardware to help enterprises easily cross the computing threshold.
■ Product Positioning
Building Enterprise-Exclusive High-Performance Computing Foundation
Providing continuous, stable, scalable, and secure computing support for AI model training, blockchain network operation, and large-scale data processing.
🎯 Value Proposition in One Sentence:
Make computing power a long-term asset for enterprises, not a short-term leased consumable.
Unlike the public cloud computing "pay-as-you-go, use-and-go" model, Magicsoft computing hardware emphasizes "assetization" and "controllability." For enterprises with long-term, stable, large-scale computing needs, building computing hardware in-house not only has lower total cost of ownership (TCO) but also keeps data within the premises, meeting compliance requirements. More importantly, hardware assets can scale smoothly with business development — one investment, multi-year benefits.
■ Core Product Portfolio
1) GPU High-Performance Servers
Product Description:
Magicsoft's GPU servers utilize mainstream high-end GPUs (NVIDIA A100 / H100 / H800, etc.), designed specifically for large model training, deep learning, and complex scientific computing scenarios. Servers support multi-GPU parallel computing internally, achieving inter-card communication bandwidth up to 900 GB/s through NVLink high-speed interconnect technology, significantly reducing communication bottlenecks during training.
When actually training large models, many enterprises find that even after purchasing multiple GPUs, training speed doesn't increase proportionally, often because inter-card communication becomes the bottleneck. Magicsoft's NVLink + NVSwitch architecture enables communication between 8 H100s to function like a giant super-GPU, with virtually zero-latency data exchange. This means your model training can upgrade from "single-card jogging" to "multi-card racing side by side," compressing training time from weeks to days, or even dozens of hours.
👉 Problems Solved:
- Slow Large Model Training → Multi-card parallel + NVLink compresses training time from weeks to days
- Insufficient Computing Resources → Single machine 8×H100 provides over 5 PFLOPS (FP16) computing power
- Obvious Performance Bottlenecks → Eliminates CPU-GPU data transfer bottlenecks, GPU utilization > 90%
2) Multi-Specification Compute Nodes
Product Description:
Different enterprises at different stages have vastly different computing power needs. Magicsoft provides tiered compute nodes, allowing customers to choose as needed, avoiding "over-investment" or "insufficient performance."
We often see two extreme situations: one is startup teams trying to save money by running models on a few gaming cards, resulting in training not converging after a week; the other is large enterprises blindly purchasing hundreds of high-end servers, only to have them idle most of the time, with electricity and depreciation costs overwhelming. Magicsoft's multi-specification compute node design is precisely to help enterprises find that "just right" configuration. Entry-level lets you quickly validate ideas, mid-level configuration supports daily production, high-performance configuration handles large-scale training, and custom configuration meets extreme scenarios. Moreover, these specifications can be smoothly upgraded between each other — you don't need to start from scratch.
| Specification Level | Typical Configuration | Applicable Scenarios | Cost-Performance Characteristics |
|---|---|---|---|
| Entry-Level | Single machine single card (RTX 4090 / A10) | Rapid model validation, algorithm prototyping, small-scale inference | Low barrier, suitable for starting |
| Mid-Level Configuration | Single machine multi-card (4×A100) | Enterprise application deployment, vertical industry model fine-tuning | Balanced performance, suitable for production |
| High-Performance Configuration | Multi-machine multi-card (8×H100 × N) | Large language model pre-training, hundred-billion-parameter models | Linear speedup ratio > 0.9 |
| Custom Configuration | Heterogeneous computing (GPU + FPGA + DPU) | Financial high-frequency, gene computing, dedicated AI inference | Extreme scenario optimization |
👉 Problems Solved:
- Large Differences in Computing Needs Across Enterprise Stages → Provides smooth upgrade paths, no need to change architecture as business grows
- Unreasonable Resource Allocation → Avoids "small horse pulling big cart" or "using a cannon to kill a mosquito," TCO reduced by 30%~50%
3) Distributed Computing Clusters
Product Description:
When single-machine computing power cannot meet large-scale training or high-concurrency inference needs, Magicsoft provides multi-node distributed computing clusters. Clusters integrate Slurm or Kubernetes as scheduling systems, supporting three distributed strategies: data parallelism, model parallelism, and pipeline parallelism, and can automatically handle node failures and task retries.
Imagine you need to train a trillion-parameter large model — even a single machine with 8 H100s would take half a year. The only way out is a cluster. But distributed training has an extremely high barrier: network topology design, parallel strategy selection, data partitioning, fault tolerance mechanisms... any problem in any link could lead to low speedup ratios or even training failure. Magicsoft's distributed computing clusters not only provide hardware but also provide validated software stacks and configuration templates. We help customers achieve near-linear speedup ratios on clusters — meaning 64 machines train at approximately 60x the speed of a single machine, not a discounted 30x. At the same time, clusters have automatic fault tolerance capabilities; even if a node goes down, tasks automatically migrate to healthy nodes and resume from the nearest checkpoint — you barely notice any anomalies.
👉 Problems Solved:
- Insufficient Single-Machine Computing Power → Supports thousand-card-level clusters, easily training trillion-parameter models
- Time-Consuming Task Queuing → Preemptive scheduling + elastic quotas, queuing time reduced by 60%
- Node Failure Interruptions → Automatic task migration + checkpoint, training recoverable
4) High-Performance Storage and Network Architecture
Product Description:
Many enterprises overlook the impact of storage and network on computing efficiency. Magicsoft provides parallel file systems + low-latency high-speed networks to ensure data I/O doesn't become a training bottleneck.
There's a common misconception: buy the most expensive GPUs and assume everything is set. But in actual operation, GPUs may spend half their time waiting idly for data — reading from disk is too slow, or receiving gradients from other nodes over the network is too slow. This is the so-called "I/O bottleneck" or "communication bottleneck." Magicsoft's high-performance storage and network architecture is designed to eliminate these hidden killers. We adopt distributed parallel file systems where data shards are written simultaneously to multiple storage nodes and can be pulled in parallel when reading, with aggregated bandwidth easily exceeding 100 GB/s. On the network side, InfiniBand or RoCE provides microsecond-level latency and 200Gbps bandwidth, ensuring All-Reduce operations between multiple nodes waste virtually no time. The end result: GPU utilization increases from 50% to over 90%, and every penny spent on computing power is worth it.
👉 Problems Solved:
- Data Becomes Bottleneck → I/O wait time reduced by 70%, GPU utilization improved to 90%+
- Slow Multi-Node Communication → High-speed network supports linear speedup ratio > 0.9
- Data Security Risks → Supports localized storage + encrypted transmission
■ Technical Capability Highlights (Differentiating Weapons)
| Capability | Magicsoft Implementation | Customer Benefits |
|---|---|---|
| Native AI Framework Support | TensorFlow / PyTorch / JAX / PaddlePaddle pre-configured | Ready to use out of the box, no adaptation needed |
| Containerized Deployment | Docker + Kubernetes integration | Environment consistency, rapid scaling |
| Multi-Tenant Resource Isolation | GPU slicing + Cgroup + Namespace | Security compliance, clear cost allocation |
| GPU Virtualization | vGPU technology (single card split into 2~8 instances) | Hardware utilization improved 3x |
| Hybrid Cloud Architecture | On-premise cluster + AWS/Alibaba Cloud/Huawei Cloud unified scheduling | Elastic peak handling, cost reduction |
| Automated Operations | Monitoring alerts + self-healing + automatic patching | Operations manpower reduced by 70% |
These technical highlights are not just talk, but best practices accumulated by Magicsoft over years of serving enterprise customers. For example, vGPU technology enabled an AI startup to virtualize 8 A100s into 32 small instances for 4 teams to develop and test simultaneously, increasing hardware utilization from 20% to 80%. Hybrid cloud architecture helped an e-commerce platform automatically overflow from local clusters to public cloud during Double 11 promotions, smoothly handling 10x traffic peaks, while using lower-cost local computing power during normal times. These capabilities are already integrated into Magicsoft's computing hardware solutions — you don't need to figure them out yourself.
■ Typical Application Scenarios (How Products Land)
✔ AI Direction
- Large Language Model (LLM) training and fine-tuning
- Multimodal model (image/video/voice) training
- Enterprise private AI deployment (RAG + fine-tuning)
Taking the financial industry as an example, many banks want to train their own private financial large models for intelligent investment advisory, risk control, and compliance review. But financial data is extremely sensitive and absolutely cannot go to public cloud. Magicsoft's computing hardware solution helps these banks build GPU clusters within their own data centers, with data never leaving the internal network, while providing training efficiency comparable to public cloud. Currently, 3 top securities firms have adopted our solution and successfully trained hundred-billion-parameter financial vertical models.
Case Study: An autonomous driving company used Magicsoft's 16-node H100 cluster to reduce perception model training time from 3 weeks to 5 days.
✔ Web3 Direction
- Blockchain full node/light node deployment
- Mining pool systems and PoW/PoS computing power networks
- Distributed storage/computing networks (Filecoin, Arweave)
Web3 project teams often need to run large numbers of validator nodes or mining machines, with extremely high requirements for hardware stability and network latency. Magicsoft's servers are specially optimized and pre-configured for mainstream public chain node software, enabling out-of-the-box usage. At the same time, we provide remote management cards and out-of-band monitoring — even if a server crashes, it can be restarted remotely, greatly reducing on-site maintenance costs.
Case Study: A public chain foundation adopted Magicsoft clusters to run 100+ validator nodes with 99.99% network stability.
✔ Enterprise Applications
- Big data real-time/offline analytics platforms
- Intelligent recommendation systems (CTR prediction, recall ranking)
- Risk control and data modeling (anti-fraud, credit scoring)
For non-AI-native enterprises, computing hardware is equally important. For example, a large retail enterprise generates hundreds of millions of user behavior logs daily and needs to run complex recommendation models and sales forecasting models. Magicsoft's computing solution not only provides a training environment but also integrates GPU inference acceleration, reducing recommendation system response time from 200ms to 30ms, directly improving user experience and conversion rates.
Case Study: An e-commerce platform using Magicsoft computing hardware reduced daily incremental training time for recommendation system models from 4 hours to 45 minutes.
■ Core Value (Why Customers Pay)
| Value Dimension | Specific Benefits |
|---|---|
| Owned Computing Assets | One-time procurement, long-term reuse, avoiding "cost out of control" in the cloud |
| Stable Performance | Dedicated hardware + network optimization, reproducible tasks, no resource contention |
| Data Controllability | Localized deployment, meeting high compliance requirements of finance, government, etc. |
| Smooth Scaling | From single machine to hundred-node clusters, seamless expansion as business grows |
| Optimal TCO | 3-year TCO 50%~70% lower than public cloud (including electricity, operations) |
Ultimately, enterprises choose computing hardware not to buy a pile of cold equipment, but to obtain stable, controllable, and cost-effective computing capabilities to accelerate business innovation. Magicsoft's value lies not only in providing hardware but also in providing full lifecycle services from selection, deployment, tuning to operations. You don't need to become a GPU expert to have expert-level computing infrastructure.
■ Comparison with Common Competitors (At a Glance)
| Comparison Item | Standard Cloud Servers | General Computing Platform | Magicsoft Computing Hardware Solution |
|---|---|---|---|
| GPU Model | Outdated/Limited | Mainstream | Latest A100/H100 in stock |
| Multi-Tenant Isolation | Weak | Average | vGPU + strong isolation |
| Distributed Training Support | Self-build required | Partial support | Native integration with Slurm/K8s |
| Storage & Network | Standard | Scalable | PB-level storage + RDMA high-speed network |
| Hybrid Cloud Capability | Public cloud only | Limited | On-premise + cloud + edge unified scheduling |
| Operations Services | None | Basic monitoring | 7×24 fully managed operations |
Many customers ask us: What's the difference between you and cloud vendors' bare metal servers? The core difference lies in "integration" and "service." Cloud vendors only provide resources; you need to handle scheduling, storage, network, monitoring, and fault tolerance yourself. Magicsoft provides an out-of-the-box computing solution with hardware, software, and services fully integrated — you just submit tasks, and we handle the rest.
■ Frequently Asked Questions (FAQs)
| Question | Answer |
|---|---|
| We don't have a professional GPU operations team. Can we still use it? | Magicsoft provides fully managed operations, including deployment, monitoring, fault handling, and patching — you just submit tasks. |
| Do existing codes need modification? | No. The platform natively supports PyTorch/TensorFlow and other frameworks; migrate and run directly. |
| How is data security ensured? | Supports private deployment with physical data isolation; strong multi-tenant isolation with projects invisible to each other; optional encrypted storage. |
| What if computing power is insufficient in the future? | Clusters support smooth expansion; new nodes automatically join the resource pool without downtime. |
| How does cost-effectiveness compare to public cloud? | For long-term use over 3 years, self-built cluster TCO is 50%~70% lower than public cloud; short-term projects can rent (Computing as a Service). |
We understand that for many enterprises, building computing hardware in-house is a significant decision. Therefore, Magicsoft provides a "try-before-you-buy" PoC service: you just tell us your business scenario and data scale, we build a small-scale cluster and run through your real models; you decide whether to formally purchase after seeing the results. This greatly reduces decision risk.
■ Next Steps (CTA)
📌 Contact Magicsoft Computing Hardware team now to receive:
- ✅ Free computing power assessment (fill in business parameters, output configuration list)
- ✅ Real customer case collection (AI/Web3/E-commerce/Finance)
- ✅ 30-day PoC trial (pay only after real business runs smoothly)
👉 Let computing hardware become a growth engine for your business, not a bottleneck.
Computing hardware is not the destination, but the starting point of intelligent applications. Magicsoft looks forward to working with you to build a future-oriented high-performance computing foundation. No matter which stage of AI exploration you are in, we can provide matching hardware solutions and service support. Feel free to communicate with us at any time.