Bare Metal for High-Performance Computing (HPC): The Definitive 2025 Guide

Bare metal provides direct access to hardware, eliminating the need for hypervisors and minimizing the risk of noisy neighbors. That’s critical when you’re running serious HPC workloads like AI training, simulations, or real-time analytics.

Virtualized environments often falter when consistency, low latency, and raw speed are non-negotiable. Hypervisors introduce unpredictability, shared I/O, noisy neighbors, and abstracted hardware control that can bottleneck performance-critical tasks like scientific simulations or LLM training. That’s where bare metal proves its value.

With no virtualization layer, you get full access to CPU, memory, and storage, exactly how HPC was meant to run. 

In this guide, we’ll show you how to match your workloads, design your cluster, and avoid costly mistakes. 

What Defines a Bare Metal HPC Stack?

A true HPC setup begins at the metal, with no layers or abstraction. You need full visibility into the hardware, including every CPU core, every DIMM slot, every GPU, and every interconnect. Bare metal gives you that.

It’s a single-tenant solution, which means no noisy neighbors and no unexpected latency spikes. That’s non-negotiable when you’re tuning MPI jobs or running memory-bound simulations.

You also get BIOS-level control. Want to disable hyper-threading? Change power states? Run custom firmware or tweak NUMA settings? Go for it. You won’t get that freedom on virtualized infrastructure.

And here’s the real-world edge: With bare metal, you know exactly what’s underneath. No guessing. No VM steals time. Just consistent, repeatable performance you can trust.

This stack isn’t for everything, but if you’re solving fluid dynamics, training LLMs, or working with edge-tuned clusters, it’s the stack you want.

Key Differences from Cloud HPC

Let’s be clear, virtualized “HPC” in the cloud isn’t real HPC. It’s a compromise.

You don’t get full CPU access. You don’t control how memory is distributed across NUMA nodes. And you’re often at the mercy of shared I/O pipelines. That’s fine for batch jobs or lightly-coupled workloads. 

However, if your workloads are tightly coupled, such as CFD, FEA, or real-time data processing, those delays add up quickly.

“Near-metal” instances might claim low overhead, but some can impact latency or IOPS, which can wreck scaling efficiency at the cluster level..

That’s where bare metal wins, because you’re working directly with the hardware, not guessing what’s underneath.

This is not just about speed. It’s about consistency, control, and knowing exactly what your code is running on, every time.

Pain Points Cloud HPC Can’t Solve (But Bare Metal Can):

• NUMA Unpredictability: You can’t optimize memory access if the VM doesn’t expose real topology.
• Shared I/O Bottlenecks: Your data-intensive job is throttled by someone else’s workload on the same host.
• Hidden Hypervisor Interference: Even with CPU pinning, the hypervisor layer introduces variability.
• Limited BIOS-Level Access: You can’t tune hardware-level performance features, because you don’t own the hardware.

Bare Metal vs Cloud for HPC: A Strategic Assessment

Choosing between bare metal and cloud for HPC isn’t just about where your code runs; it’s about how well it performs when it matters most. Here’s a side-by-side comparison of where each option stands, allowing you to match your workloads with the right infrastructure.

HPC Workload Profiles That Demand Bare Metal Infrastructure

Some workloads don’t tolerate compromise. If you’re working with high-performance computing at scale, bare metal isn’t just a nice-to-have; it’s foundational. When latency, memory locality, or multi-GPU bandwidth are mission-critical, virtualization gets in the way. These six HPC profiles consistently benefit from direct-to-hardware deployment. Here’s what that looks like in the field.

1. Scientific Research and Simulation

Workloads: Weather modeling, molecular dynamics, astrophysics, materials analysis

Why Bare Metal: These workloads rely on tightly coupled MPI processes, where latency between nodes determines simulation accuracy and runtime. Hypervisors disrupt NUMA awareness and message-passing performance.

Infrastructure Notes: Bare metal enables you to run NUMA-tuned nodes with high-bandwidth DDR5 memory and install parallel file systems, such as Lustre or BeeGFS, for fast checkpointing. You can also control BIOS-level tuning for cache, memory prefetch, and power states, crucial for reproducible scientific workloads.

2. Deep Learning & Large-Scale AI

Workloads: LLM training (e.g., GPT), reinforcement learning, computer vision, multi-modal transformers

Why Bare Metal: Training on 8×H100 or A100 GPUs per node requires NVLink or NVSwitch, and that kind of direct interconnect bandwidth doesn’t survive virtualization. Virtualized environments also limit GPU pass-through, block low-level tuning, and introduce unpredictable latency between GPUs.

Infrastructure Notes: You need RDMA-capable GPU clusters with NVMe RAID arrays for high-throughput checkpointing. Add BeeOND or similar scratch layers for intermediate training data. The performance difference is massive, especially during gradient sync or distributed backpropagation.

3. Blockchain Validation & Web3 Infrastructure

Workloads: Validator nodes, ZK rollup proofs, decentralized indexing (The Graph), archival node hosting

Why Bare Metal: Web3 infrastructure is uptime-sensitive and storage-heavy. Even small jitter or I/O throttling can cause consensus failures or force re-syncs. Block processing demands 24/7 stability, with high IOPS and low-latency network throughput for timely validation and peer communication.

Infrastructure Notes: Equip bare metal machines with ECC RAM, isolated NICs (for DDoS mitigation), and local NVMe storage to host blockchain ledgers and run parallel state transitions. Bare metal also gives you deterministic access to cores, key for ZK circuits, and proof generation.

Pro Insight: We’ve seen cloud-hosted nodes drop blocks due to unpredictable VM throttling, an issue that’s fatal for validator uptime and economic rewards.

4. Genomic Sequencing & Bioinformatics

Workloads: Whole-genome alignment, RNA-seq, CRISPR simulations, protein folding

Why Bare Metal: Genomics pipelines, such as GWAS and de novo assembly, require massive memory, often exceeding 1 TB. High-memory cloud VMs are costly. They also throttle IOPS and limit CPU bursts. Bare metal skips all that. It delivers consistent performance for every sequencing run.

Infrastructure Notes: You need servers with multi-core CPUs (48+ threads), all-NVMe scratch, and memory-optimized BIOS settings for paging and NUMA alignment. It’s also common to pair bare metal with local SLURM schedulers to manage job queues at scale.

5. Financial Modeling & Risk Simulation

Workloads: Real-time market forecasting, Monte Carlo simulations, stress testing, portfolio optimization

Why Bare Metal: Quant workloads depend on ultra-low latency, regulatory isolation, and clock consistency. Even microsecond jitter from shared cloud infrastructure can skew predictions and break compliance. Financial modeling also demands deterministic hardware behavior, down to CPU cache layouts.

Infrastructure Notes: Deploy with RDMA-enabled NICs for real-time market data feeds, encrypted NVMe storage, and core isolation for trading engines. Regulatory frameworks like PCI-DSS and SOC2 are easier to meet with physical isolation and full system control.

6. Big Data Analytics & Real-Time Processing

Workloads: Petabyte-scale warehousing (Presto, ClickHouse), fraud detection, behavioral analytics, log processing

Why Bare Metal: These pipelines need consistent read/write throughput and CPU-to-disk ratios that cloud VMs can’t guarantee. Virtual storage often introduces latency cliffs and disk contention, especially under concurrent I/O.

Infrastructure Notes: Build storage-intensive nodes with tiered NVMe caching, local object storage pools, and optimized CPU-to-I/O configurations for engines such as Spark, Trino, or Hadoop. Bare metal gives you the IOPS headroom to run massive joins, deduplication passes, and real-time dashboards, without stalls. 

Performance Engineering: How Bare Metal Maximizes Throughput

When you’re tuning for throughput, every layer you strip away makes a difference.

Latency Elimination Through Hardware Access

Hypervisors create overhead. Bare metal removes them entirely.

• You get direct access to CPU registers, cache lines, and memory bandwidth.
• PCIe devices like GPUs, NICs, and SSDs connect straight, no passthrough hacks.
• Technologies like RDMA and NVLink operate at full bandwidth, with minimal CPU interrupt handling.

When you’re running latency-sensitive workloads, such as distributed AI training or MPI compute, this difference adds up quickly.

Precision Scheduling and NUMA Control

NUMA misalignment kills performance. Cloud instances often mask or abstract away real memory and CPU layouts. Bare metal lets you correct that.

• Tune BIOS to align memory zones with CPU groups.
• Pin threads to specific cores and cache segments.
• Reserve memory regions for MPI processes or memory-hungry apps.

Storage and I/O Optimization

Storage isn’t just about capacity. It’s about speed and consistency under load.

• Utilize PCIe Gen4 or Gen5 NVMe drives in RAID 0 for ultra-fast scratch space.
• Implement NVMe-over-Fabrics to extend low-latency storage across nodes.
• Integrate parallel file systems like BeeGFS, Lustre, or Spectrum Scale directly, without dealing with virtualization workarounds.

A Dell-based bare-metal NVMe RAID setup delivered 2.9× better performance than previous-gen SATA-based clusters for mixed read/write HPC workloads.

Designing a Bare Metal HPC Cluster (From Node to Network)

Building a bare-metal HPC computing cluster isn’t just about stacking servers. It’s about tuning every layer, from CPU lanes to I/O paths, for consistency, speed, and scale. Here’s how to design one that doesn’t just run workloads, it moves them.

Compute Node Architecture

Start with the right CPU foundation. Your compute nodes are the backbone of your cluster, and weak links here become apparent quickly at scale.

• Go with 2× AMD EPYC Genoa or Intel Xeon Gen5 for high thread count and wide memory lanes.
• 512–2048 GB of DDR5 ensures headroom for memory-bound tasks like FEM simulations or RNA-seq pipelines.
• Add PCIe Gen5 slots to handle high-speed GPUs, FPGAs, or smart NICs without bottlenecks.
• Avoid mixing CPU generations across nodes, consistency makes tuning and orchestration cleaner.

GPU & Accelerator Considerations

Not all workloads are CPU-bound. LLMs, computer vision, and seismic modeling all lean heavily on accelerators.

• Utilize 4–8 NVIDIA A100 or H100 GPUs per node, equipped with NVLink/NVSwitch for rapid inter-GPU communication.
• For non-NVIDIA use cases, consider AMD MI300X for dense inference or FPGAs for specialized logic (ZKPs, genomics).
• Cooling matters. 8-GPU nodes can easily hit 5–7kW, design racks accordingly.

Pro Insight: If you’re training transformer models with more than 10B parameters, NVSwitch isn’t optional, it’s required to avoid communication stalls.

High-Speed Network Fabric

The faster your nodes talk, the faster your jobs run. Your interconnect defines your upper limit for scalability.

• Deploy RDMA over InfiniBand HDR or RoCEv2, both deliver low-latency, high-throughput communication for MPI workloads.
• Standard setup: Dual 200 Gbps NICs per node for fault tolerance and bandwidth.
• Keep an eye on CXL (Composable Memory Fabric). It’s not production-ready everywhere yet, but it’s promising for decoupling compute from memory scaling.
• Avoid single-NIC architectures. One dropped packet during a long simulation? That’s a wasted compute cycle.

Storage & File System Layer

Your storage layer moves more than data, it moves the cluster. Poor disk performance slows everything, regardless of how fast your GPUs are.

• Equip nodes with 6.4TB–25TB of NVMe SSDs. Run them in RAID 0 for scratch space, fast, disposable, and local.
• Use BeeGFS or Lustre for distributed storage. Both scale horizontally and integrate well with HPC schedulers.
• Want a smooth recovery and better metadata ops? Use dedicated metadata servers in your storage tier.

Pro Pattern: Use dedicated control nodes for PXE booting, Slurm orchestration, and cluster health checks. Keep storage nodes separate from compute to avoid noisy neighbor effects.

Operational Stack: Orchestration, Scheduling, and Monitoring

Bare metal gives you the performance. However, without orchestration, your cluster can become chaotic. Here’s how to keep it lean, predictable, and scalable from job dispatch to deep profiling.

Job Schedulers and Resource Managers

Slurm is still the go-to. It’s open-source, battle-tested, and handles everything from GPU affinity to job arrays. If you’re in research or working in a hybrid academic/commercial HPC setting, start here.

PBS Pro and IBM LSF are strong in grid and enterprise batch systems. LSF excels in long-standing financial and pharmaceutical environments where policy control is crucial.

Want to go hybrid? HKube and Volcano bring Slurm-style scheduling to Kubernetes so that you can run AI inference or microservices next to your HPC jobs. Just be ready to tune both stacks.

Pro insight: Avoid scheduler sprawl. Select one model and standardize it across your compute nodes to reduce debugging and administrative overhead.

Node Provisioning & Deployment Tools

Bare metal provisioning is easier than ever, if you choose the right combo.

• Use PXE boot to auto-provision OS images.
• Combine with Ansible for post-boot config.
• Pair with MAAS for state tracking and lifecycle control.

For a plug-and-play approach, consider Bright Cluster Manager or OpenHPC. These give you node orchestration, update rollouts, and provisioning templates in one place. Be aware that vendor lock-in can creep in quickly.

Expert tip: Enforce strict provisioning templates. Even a small drift (e.g., BIOS settings or kernel versions) across nodes breaks cluster scaling. Homogeneity isn’t just nice, it’s required.

Monitoring, Profiling, and Optimization

Performance isn’t set-and-forget. You need visibility into everything from CPU stalls to queue delays.

• Grafana and Prometheus provide real-time metrics with custom dashboards. Track GPU thermals, job queue lengths, IOPS, and memory usage.
• ClusterShell lets you run health checks, reboots, or fixes across hundreds of nodes with one command. Script everything.
• For app-level tuning, use Intel VTune for CPU-bound code and NVIDIA Nsight for CUDA kernels, inter-GPU traffic, or memory bottlenecks.

What’s often missed? File system metrics. Healthy node statistics can mask slow BeeGFS or Lustre performance. Always monitor I/O depth, open file handles, and storage queue times to ensure optimal performance.

Is Bare Metal Right for Your HPC Project?

Not every workload needs bare metal, but the right ones grow on it. Ask yourself these five questions to decide if it’s worth the investment:

• Are your jobs compute- or memory-bound?

Bare metal gives you full access to CPU cores and memory lanes, no interference, no sharing.

• Do you need sustained GPU availability?

Shared clouds throttle and rotate resources. Bare metal keeps your multi-GPU nodes online, 24/7.

• Do you require latency <10µs between nodes?

With RDMA and direct InfiniBand, bare metal hits ultra-low latencies that virtual networks can’t match.

• Are your workloads stable and continuous?

Bare metal pays off when jobs run often and predictably. It’s built for long-haul performance, not one-off spikes.

• Do you need full-stack visibility and tuning?

BIOS control, NUMA alignment, GPU scheduling—it’s all yours with bare metal. No hidden layers, no guesswork.

Final Words

High-performance computing isn’t just about raw specs; it’s about precision, consistency, and control. Bare metal gives you all three. Whether you’re training large models, running scientific simulations, or powering real-time analytics, shared infrastructure will hinder your progress. 

With RedSwitches, you get direct access to high-performance CPUs, GPUs, and NVMe storage, no virtualization, no bottlenecks. Our global bare metal infrastructure is built for teams that need results, not excuses.

Ready to deploy faster, scale smarter, and run with full control?

Explore RedSwitches Bare Metal Servers and launch your HPC project with the performance it deserves.

Frequently Asked Questions

Q. What is bare metal in telecom?

In telecom, bare metal refers to dedicated physical servers used for running network functions, 5G workloads, or signal processing without virtualization. It gives full control over latency, bandwidth, and hardware.

Q. What is a bare metal cluster?

It’s a group of physical servers connected to act as one system. Each node runs without a hypervisor, giving direct access to CPU, memory, and I/O for high-performance tasks.

Q. What does bare metal mean in the cloud?

Bare metal in the cloud means renting a full physical server from a provider. You get no virtualization and no shared tenants, just raw, dedicated hardware.

Q. What makes bare metal ideal for latency-sensitive scientific simulations?

Bare metal removes hypervisors and VM overhead, allowing direct access to memory and CPU. This reduces latency to under 10 microseconds, crucial for tightly-coupled workloads like MPI.

Q. How can I customize a bare metal server for specific HPC tasks?

You can configure BIOS, disable hyper-threading, assign NUMA zones, or install real-time kernels. You also choose exact CPUs, memory, and GPUs to match your workload.

Q. Why are bare metal servers more reliable than virtualized options in HPC?

They offer full hardware isolation, predictable performance, and no noisy neighbors. With bare metal, there are no surprise slowdowns or resource sharing during compute-heavy jobs.