DEV Community: Shannon Dias

Ryzen 7 vs Ryzen 9 for Dedicated Game Servers: Which Do You Really Need?

Shannon Dias — Fri, 24 Jul 2026 05:38:04 +0000

If you are architecting a machine to host a heavily modded Minecraft network, a massive Rust instance, or an ARK cluster, your CPU choice is the single most critical factor in keeping your server's tick rate stable.

Desktop gaming and server hosting are entirely different workloads. Let's look at the hardware from a server admin's perspective.

Single-Threaded Engines vs. Multi-Tasking

Most modern game engines are notoriously bound by single-threaded performance. The server software relies primarily on a single CPU core to calculate the game's main simulation loop: player movements, physics, entity AI, and block updates.

Ryzen 7 (The Sprinter)

For hosting a single, high-population game server, the Ryzen 7 (specifically X3D models like the 7800X3D or 9800X3D) is the superior choice.

The 3D V-Cache Advantage: Server software constantly needs to retrieve small bits of entity data. Keeping that data on the CPU's massive L3 cache (up to 96MB) rather than fetching it from the system RAM drastically reduces latency.

Ryzen 9 (The Bodybuilder)

If you plan to run multiple heavy server instances simultaneously on the same machine, the high core count of the Ryzen 9 (like the 7950X or 9950X) makes it the superior, heavy-duty workhorse.

Up to 16 physical cores / 32 Threads prevents resource fighting between containerized servers.

The "Overkill" Debate

If you are only hosting one server for a dozen friends, a Ryzen 9 is completely overkill. You will be paying a premium for 16 cores, but your game server will only use 2 to 4 of them. However, if you are running a lobby server alongside three survival worlds, a Ryzen 9 is exactly what you need.

Want to dive into real-world game engine behaviors and RAM requirements?

👉 Read the full technical breakdown on the Fit Servers Blog

Startup Guide to High-GPU Dedicated Servers for AI Training

Shannon Dias — Fri, 24 Jul 2026 04:44:27 +0000

The artificial intelligence boom has fundamentally changed how startups build, scale, and budget. If your engineering team is training Large Language Models (LLMs) or processing massive computer vision datasets, virtualization overhead in public clouds can quickly deplete your capital.

Why Transition to Dedicated GPU Servers?

Cost Predictability: Avoid unpredictable public cloud egress fees and hypervisor markups. Sustained training on bare metal typically saves startups 50% to 70%.
Raw Performance: No hypervisor means direct, unthrottled access to PCIe lanes, NVMe storage, and high-speed GPU clusters.
Data Sovereignty: Single-tenant environments make compliance with strict privacy regulations (HIPAA, GDPR, SOC2) seamless.

Choosing the Right NVIDIA GPU

NVIDIA H200 (141GB HBM3e): Unmatched memory bandwidth for training massive LLMs (70B+ parameters).
NVIDIA H100 (80GB HBM3): Raw FP8 compute speed for foundational models and heavy fine-tuning.
NVIDIA L40S (48GB GDDR6): Cost-efficient versatility for generative AI and multi-modal fine-tuning.
NVIDIA L4 (24GB GDDR6): High energy efficiency built specifically for AI inference and serving.

Read the full comprehensive guide to look into power/cooling requirements, avoiding CPU bottlenecks, and cluster orchestration: FitServers Blog

Why Atlanta Is the Ultimate Hub for Enterprise Dedicated Servers

Shannon Dias — Fri, 17 Jul 2026 10:10:39 +0000

Why Location Is as Important as Hardware

Enterprise teams evaluating dedicated server providers tend to focus on CPU specs and RAM configs first — but the physical location of that hardware shapes latency, disaster resilience, and long-term uptime just as much. Atlanta has become one of the strongest choices in North America for exactly this reason.

What the full guide covers

Strategic geography — Tier-1 fiber routing, direct LATAM gateway access, and why an inland location matters for disaster recovery planning
Latency benchmarks — real RTT numbers to Miami, NYC, Chicago, London, Frankfurt, and São Paulo
Hardware configurations — when to choose high-frequency single-threaded CPUs vs. dense multi-core AMD EPYC/Intel Xeon builds vs. enterprise GPU servers for AI/ML training
Storage tiers — NVMe vs. SATA/SAS SSD vs. high-capacity SATA arrays
Unmetered bandwidth — what the term actually guarantees, and where it matters most (CDNs, streaming, VPN infrastructure)
Compliance standards — Tier III, SOC 2 Type II, and PCI-DSS, and why they matter for FinTech and e-commerce deployments

Read the full breakdown

The complete guide, including the full latency table and FAQ section, is available here:

👉 https://www.fitservers.com/blogs/atlanta-dedicated-servers-hub/

The Ultimate Guide to Installing PyTorch on a GPU Server

Shannon Dias — Wed, 08 Jul 2026 11:05:49 +0000

If you are diving into deep learning, training large language models (LLMs), or running complex AI workloads, a GPU dedicated server gives you the bare-metal performance required to maximize compute efficiency. However, to harness the full power of your hardware, you need to configure your software stack correctly.

At Fit Servers, we've put together a comprehensive guide to help developers and AI researchers set up their environments from scratch.

What You Will Learn

In this guide, we walk you through the exact steps to install and configure:

System Preparation: Purging old drivers and updating Ubuntu 22.04 LTS.
NVIDIA Drivers: Installing stable production drivers (v560).
CUDA Toolkit & cuDNN: Setting up CUDA 12.4 for modern PyTorch builds and compiling custom kernels like FlashAttention.
Environment Management: Using Miniconda to isolate your Python dependencies.
PyTorch Installation: Fetching the latest CUDA-compatible wheels.
Benchmarking: A custom Python script to verify your TFLOPS and ensure your GPU is fully recognized.

Why Bare-Metal?

Virtualization overhead can silently eat away at your model training times. Setting up PyTorch directly on a bare-metal GPU server ensures 100% of your compute goes toward your matrix multiplications, not hypervisor bloat.

👉 Read the full, detailed guide with all code snippets on the Fit Servers Blog

How to Install Coolify on an Ubuntu 24.04 Dedicated Server

Shannon Dias — Fri, 03 Jul 2026 10:59:57 +0000

Running Coolify on a dedicated server running Ubuntu 24.04 LTS gives you complete control over your infrastructure, maximum performance, and the ability to scale your deployments cost-effectively.

Coolify is a powerful, open-source, and self-hostable Platform as a Service (PaaS) that serves as an excellent alternative to Heroku, Netlify, and Vercel. It allows you to manage servers, applications, and databases on your own hardware using Docker—without the risk of vendor lock-in.

What You Will Learn in Our Guide:

System Preparation: How to properly add swap space (crucial for Docker memory spikes during builds).
UFW Firewall Security: How to secure your server without conflicting with Docker's internal routing.
Automated Installation: Deploying the core containers securely.
Custom Domains: Securing your dashboard with Let's Encrypt SSL.

At Fit Servers, we provide the enterprise-grade bare-metal hardware you need to run your own PaaS reliably.

👉 To get the complete code snippets and step-by-step instructions, read the full tutorial on our blog: Read More: How to Install Coolify on Ubuntu 24.04

The Ultimate Guide to Configuring Ceph Storage for Proxmox on Dedicated Hardware

Shannon Dias — Fri, 03 Jul 2026 10:15:43 +0000

Deploying a hyper-converged Proxmox VE and Ceph environment gives you a highly scalable, self-healing storage cluster. However, Ceph is unforgiving of poor hardware choices and bad network design.

This guide will walk you through deploying Ceph on dedicated hardware the correct way, designed for production environments.

What Most Tutorials Get Wrong (The Pitfalls)

Before touching a single command line, you must understand the common traps that ruin most amateur Ceph deployments. Many generic tutorials recommend shortcuts that will inevitably cause data corruption or massive latency spikes under load.

Mistake 1: Recommending Consumer SSDs. Consumer drives lack Power-Loss Protection (PLP). Ceph relies heavily on synchronous writes for its Write-Ahead Log (WAL) and RocksDB. Without PLP, consumer SSDs will drop to single-digit IOPS during sync operations, stalling your entire cluster. Always use Enterprise SSDs/NVMe.
Mistake 2: Leaving Hardware RAID Enabled. Ceph is a software-defined storage solution that requires direct, block-level access to your raw disks to manage smart health checking and data distribution (BlueStore). Always flash your storage controller to IT Mode (HBA) so disks are passed through as raw devices (JBOD).
Mistake 3: Poor Physical Network Isolation. Running Corosync, VM traffic, and Ceph replication over the same network interface is a recipe for disaster. Ceph replication will easily saturate your links during recovery events, starving VMs of network access and causing Corosync to lose quorum (which can reboot your nodes).
Mistake 4: Sacrificing Replicas for Usable Space. Lowering your pool settings to size=2, min_size=1 to get more usable space guarantees data loss. If one node drops and a single bit-rot event occurs on the remaining drive, your data is gone. Always use size=3, min_size=2.

1. Hardware & Network Prerequisites

To deploy a highly available, high-performing Ceph cluster, you need a minimum of three identical Proxmox nodes.

Component	Minimum Requirement	Enterprise Best Practice
Nodes	3 Physical Servers	5+ Physical Servers
CPU	8 Cores per node	16-32 Cores (~1 core per OSD)
RAM	32 GB RAM	64 GB+ (~4-8 GB per OSD)
Boot Drive	2x SSDs in ZFS Mirror	2x Enterprise NVMes in ZFS Mirror
Ceph Disks	3x Enterprise SSDs per node	4-8x Enterprise NVMe/SSDs
Network	2x 10GbE NICs (Bonded)	4x 10GbE/25GbE+ (Dedicated Links)

2. Network Architecture

A healthy Proxmox and Ceph cluster requires strict network isolation.

Corosync Network: The absolute most critical network. This should be a dedicated, physically isolated network for Proxmox cluster heartbeats. Ideally, configure multiple rings (Ring 0 and Ring 1) on separate physical interfaces to prevent quorum loss if a switch reboots.
Management Network: Used for Proxmox GUI access, SSH, and backups.
VM Public Network: Bridged network for your virtual machines to access the internet/LAN.
Ceph Network (Public & Cluster): If you only have two 10GbE NICs per node, it is highly recommended to put them in an LACP Bond (Active/Active) for a unified Ceph network, rather than dedicating one NIC to Public and one to Cluster. True separation of Ceph Public and Ceph Cluster networks requires four distinct physical 10/25GbE links per node to avoid a single point of failure.

Network Optimization Note: For maximum performance, configure Jumbo Frames (MTU 9000) on your switches and the Proxmox network interfaces dedicated to Ceph. Ensure this is identical across the entire data path.

3. Step-by-Step Configuration

This guide assumes you have already installed Proxmox VE 8.x on all nodes and have configured the underlying network interfaces.

Step 1: Pre-flight Checks

On every node, ensure your packages are updated, the time is perfectly synchronized via NTP/Chrony (crucial for Ceph monitors), and that the /etc/hosts file contains the correct IP resolutions for all nodes.

apt update && apt full-upgrade -y

Step 2: Install Ceph Packages

Proxmox simplifies Ceph installation by integrating it directly into its package manager. Run the following on every node in the cluster. When prompted, select the Enterprise or No-Subscription repository depending on your Proxmox licensing.

pveceph install

Step 3: Initialize the Ceph Cluster

Run this command on your first node only. This creates the base configuration file and defines your Ceph network. Replace the subnet with your actual Ceph network IP range.

pveceph init --network 10.15.15.0/24

Step 4: Deploy Monitors (MON) and Managers (MGR)

Ceph Monitors (MON) track the cluster state, while Managers (MGR) handle metrics and the dashboard. You need an odd number of monitors (3 or 5) to establish quorum and maintain High Availability (HA). Note: If you use the Proxmox Web GUI to initialize Ceph, it often provisions the first MON and MGR automatically.

If doing it via CLI, on Node 1:

pveceph createmon
pveceph createmgr

On Node 2 and Node 3:

pveceph createmon

Step 5: Provision the OSDs (Storage Daemons)

An OSD (Object Storage Daemon) is the worker process that interacts with your physical drives. You must create one OSD per physical disk dedicated to Ceph. Ensure these disks have no existing partitions. Replace /dev/nvmeXn1 with your actual drive identifiers (found via lsblk). Run this on each node:

pveceph osd create /dev/nvme0n1
pveceph osd create /dev/nvme1n1

Step 6: Create the Ceph Pool

The pool is where your virtual machine disks will actually live.

Navigate to the Proxmox GUI: Datacenter -> Ceph -> Pools.
Click Create.
Name: vm_storage (or your preference).
Size: 3 (Data is written to 3 separate nodes).
Min Size: 2 (Requires at least 2 nodes to acknowledge a write to prevent data corruption).
Enable Add as Storage to make it instantly available to Proxmox VMs.

4. Post-Installation Tuning & Best Practices

Disable VM Disk Cache: When deploying VMs on Ceph block storage (RBD), set the VM Disk Cache to None. Ceph handles its own caching natively; enabling Proxmox caching on top of it adds overhead and latency.
Use VirtIO-SCSI Single: In your VM hardware settings, set the SCSI controller to VirtIO-SCSI single and check the IO Thread and Discard boxes. This allows multi-threaded I/O processing, drastically improving IOPS.
Monitor Memory Spikes: By default, Ceph targets around 4GB of RAM per OSD. However, during cluster recovery, rebalancing, or deep scrubbing, memory usage can spike significantly beyond this target. Ensure your hosts have ample overhead RAM so your VMs are not starved during storage maintenance events.

Ready for Uncompromising Performance?

Running a high-performance, hyper-converged Proxmox and Ceph cluster requires bare-metal hardware that won't bottleneck your storage. Avoid the headaches of underpowered networks and consumer-grade drives.

🔗 Read the original post and explore our dedicated hardware solutions here: Fit Servers - How to Deploy Ceph on Proxmox

Stop Using Outdated Proxies: How to Set Up a Modern Shadowsocks-Rust Server

Shannon Dias — Wed, 01 Jul 2026 11:31:37 +0000

Setting up a private proxy can drastically improve your privacy and help you bypass restrictive firewalls. However, the Shadowsocks ecosystem has evolved significantly. If you follow an older tutorial, you risk exposing your data to active probing and suffering from poor network speeds.

Most tutorials still recommend deprecated versions (like Python or libev) and vulnerable ciphers.

In our latest guide, we cover the modern standard: Shadowsocks-Rust.

What You Will Learn:

Network Optimization: How to enable Google's TCP BBR congestion control for maximum throughput.
Modern Cryptography: Generating 32-byte Base64 keys for 2022-blake3-aes-256-gcm ciphers.
Evading Detection: Why you need randomized high-number ports to defeat active firewall scanners.
Daemonization: Setting up a Systemd service to keep your proxy running persistently.

We’ve broken down the exact terminal commands you need for Ubuntu 24.04 / Debian 12 to get your secure tunnel up and running in minutes.

To get the full step-by-step code snippets and configure your dedicated server perfectly, read more and visit the blog link: Read the full tutorial on Fit Servers

The Production-Ready Guide to Self-Hosting LLaMA 3 on a GPU Dedicated Server

Shannon Dias — Fri, 26 Jun 2026 09:03:33 +0000

Most online AI guides share a major flaw: they are written for local development on a laptop rather than enterprise deployments on dedicated servers. If you rely on a basic script running the raw transformers library, concurrent production traffic will quickly cause memory bottlenecks and crashes.

If you are ready to secure your data privacy and eliminate third-party API rate limits, you need a high-throughput setup using vLLM and Docker on bare-metal infrastructure.

The Core Tech Stack & Gotchas

vLLM vs. Ollama: While Ollama is excellent for quick tests, vLLM utilizes PagedAttention to eliminate memory fragmentation, making it the industry standard for handling multi-user requests simultaneously.
The Docker Firewall Loophole: Relying purely on UFW? Docker natively alters iptables and will bypass your standard rules, exposing port 8000 to the public web. You must explicitly bind the container to 127.0.0.1.
Memory Overhead: You cannot just look at model weights. You must preserve a 20% VRAM headroom buffer to account for Key-Value (KV) caching under active user loads.

Hardware Reference Matrix

Model Version	Precision	VRAM Required	Ideal Server Setup
LLaMA 3 8B	BF16 (Uncompressed)	~16 GB	1x RTX 4090 (24 GB) / RTX 5090 (32 GB)
LLaMA 3 70B	4-bit Quantized	~40 GB	2x RTX 3090/4090 (48 GB total)
LLaMA 3 70B	BF16 (Uncompressed)	~140 GB	2x A100 80GB (160 GB)

In our full deployment guide, we walk you through installing the NVIDIA Container Toolkit, adjusting the Docker daemon JSON parameters, authenticating with Hugging Face token gates, and optimizing the --tensor-parallel-size argument for multi-GPU setups.

For the complete step-by-step bash scripts and configurations, read more visit the tutorials link: https://www.fitservers.com/tutorials/howto/deploy-llama-3-vllm-dedicated-gpu/

The Enterprise Guide to Installing NVIDIA Drivers and CUDA on Ubuntu Server

Shannon Dias — Fri, 26 Jun 2026 07:33:26 +0000

If you are hosting deep learning models, rendering complex graphics, or running high-performance computing (HPC) workloads, getting your GPU server configured correctly is the foundational first step.

Many tutorials online recommend using default Ubuntu repositories or editing local user files to set up CUDA. However, for a dedicated production server, those methods often lead to outdated packages, broken cron jobs, and application errors.

What this production-grade guide covers:

Pre-Flight Checks: Identifying your GPU hardware (lspci) and installing matching Linux kernel headers.
Purging & Blacklisting: Dropping legacy conflict libraries and neutralizing the Nouveau driver.
Official Repositories: Integrating the official NVIDIA network keyring (avoiding outdated Ubuntu PPAs).
System-Wide Environment Mapping: Creating executable profile scripts in /etc/profile.d/ so background daemons can access your hardware.

Don't let bad driver setups throttle your development velocity.

For read more visit the tutorials link: https://www.fitservers.com/tutorials/howto/install-nvidia-cuda-ubuntu-server/

Beyond `ollama run`: Production-Ready DeepSeek R1 Deployment with vLLM and Nginx

Shannon Dias — Fri, 26 Jun 2026 07:05:08 +0000

Tired of simplified tutorials that show you how to run DeepSeek R1 on a personal laptop but leave your dedicated server completely exposed to the web? Let's build a secure, high-throughput enterprise deployment.

In this comprehensive guide, we build a production-grade stack using Ubuntu 22.04, Docker, vLLM, and Nginx.

The Stack Architecture

Inference Engine: vLLM (utilizing PagedAttention for continuous batching).
Model: neuralmagic/DeepSeek-R1-Distill-Llama-70B-FP8-Dynamic (optimized for single-node multi-GPU VRAM constraints).
Security Layer: UFW firewall rules combined with an Nginx reverse proxy enforcing secure Bearer token authentication.

Critical Deployment Elements Covered

Fixing Docker's Firewall Bypass: How to safely bind your model port to 127.0.0.1 so your expensive GPU isn't open to the public internet.
Shared Memory Configuration: Allocating adequate --shm-size to support multi-GPU NCCL communication and eliminate Out of Memory (OOM) crashes.
Nginx SSE Streaming Fixes: Disabling proxy_buffering and extending timeouts to handle token-by-token text generation seamlessly.

For the full step-by-step code blocks, configuration files, and commands, read more by visiting the tutorial link: Host DeepSeek R1 on a Dedicated Server with vLLM

Production-Ready Ollama: Deploying GGUF LLMs on CPU-Only Ubuntu 24.04 Servers

Shannon Dias — Sat, 20 Jun 2026 10:00:48 +0000

Running Large Language Models locally is no longer gatekept by expensive cloud GPUs. By leveraging 4-bit quantized GGUF models via Ollama, you can easily spin up robust AI capabilities on budget-friendly, CPU-only dedicated servers.

But if you follow standard quick-start guides, you're likely creating massive security vulnerabilities and introducing severe processing bottlenecks.

We just published an end-to-end deployment blueprint over at Fit Servers that addresses real-world, production-level configurations.

What We Cover in the Blueprint:

Zero-Trust Firewalling: Blocking port 11434 globally with UFW and utilizing local SSH port forwarding (ssh -N -L 11434:localhost:11434 user@ip) to eliminate unauthenticated public API access.
NUMA & Thread Tuning: Running lscpu to isolate physical cores, configuring systemd overrides (systemctl edit ollama.service), and setting OLLAMA_NUM_THREADS accurately to bypass hyperthreading overhead.
Mitigating RAM Thrashing: Why we highly recommend disabling system swap (sudo swapoff -a) to let Linux's OOM Killer catch memory leaks instantly rather than grinding your OS to a halt via disk-thrashing.

Ensure your proprietary data stays internal while keeping operating overhead low.

📖 Read the complete step-by-step terminal commands here

Top 10 Linux Distros for Dedicated Servers in 2026 🐧

Shannon Dias — Fri, 12 Jun 2026 08:46:58 +0000

When you rent a dedicated server, the hardware spec sheet is only half the story. The Linux distribution you flash onto that bare metal dictates your security patch lifecycle, available software dependencies, and whether your automation scripts or control panels will even function correctly two years out.

Choosing the wrong upstream platform is an expensive mistake to fix once traffic hits production.

Here is a quick snapshot of how the server landscape shapes up in 2026 for production stacks:

⚡ The Heavy Hitters

Ubuntu Server 24.04 LTS: The undisputed king for DevOps pipelines and rapid container deployment. Excellent community tutorials, though you'll need to account for Canonical's heavy reliance on Snaps.
Debian 12 "Bookworm": Ultra-lean, minimalist, and brutally stable. If you want a server that runs for years without demanding intervention, this is your baseline.
AlmaLinux 9 & Rocky Linux 9: The twin pillars of enterprise RHEL compatibility. Essential if you run cPanel/WHM or are still completing your migrations away from legacy CentOS 7 environments.
Oracle Linux 9: An overlooked powerhouse for high-availability setups, providing enterprise-grade live kernel patching (Ksplice) entirely for free.

🏢 Specialized Workloads

For specialized infrastructure providers, general-purpose distros won't cut it. Multi-tenant hosting environments running high-density accounts rely heavily on platforms like CloudLinux OS to enforce resource isolation (LVE) and virtualized filesystems (CageFS) at the OS level.

Don't pick an operating system purely out of habit. Analyze your support cycle expectations, documentation needs, and vendor-certification requirements before provisioning your bare metal hardware.

📖 For the full technical breakdown, support lifecycles, and direct comparison matrix, read more and visit the tutorials link: Fit Servers - Top 10 Linux Distros Guide