Frequently asked questions
Certified Fit Audit Mechanics & Cost
How does the Certified Fit Audit work, and is it really free?
Our goal is for every customer to be totally satisfied with their purchase. If this isn't the case, let us know and we'll do our best to work with you to make it right.
HMRC R&D Tax Compliance
How do your workstations support UK HMRC R&D claims?
Consumer PC receipts often fail HMRC audits for R&D tax credits. We provide bundled, technical utility statements and proper compliance documentation with every build, proving the hardware is specifically provisioned for R&D inference and training workloads, protecting you from tax leakage.
High-VRAM GPU Availability
Which high-VRAM GPUs are available for local inference?
We supply architectures configured specifically to eliminate "Out of Memory" (OOM) errors and avoid recurring cloud compute costs. This includes NVIDIA Blackwell, H100, and RTX 6000 Ada Generation GPUs (ranging from 48GB to 192GB VRAM per card), scaled to your specific parameter requirements.
PCIe Lane Allocation & Bandwidth
How do you handle PCIe lane allocation for multi-GPU setups?
Underspecced PCIe lanes are a primary cause of multi-GPU bandwidth throttling. During the audit, we validate your required lane allocation and specify platforms with sufficient native PCIe 5.0 lanes (such as AMD Threadripper Pro, EPYC, or Intel Xeon W-series). We also specify NVLink configurations for distributed inference on 70B+ parameter models where direct GPU-to-GPU bandwidth is critical.
ECC Memory Validation
Are your systems built with ECC DDR5 memory?
Yes. Where the platform supports it, we specify ECC-validated DDR5 memory, including channel population strategy and speed grade validation. ECC is mandatory for our sustained training and long-running inference builds to ensure memory error correction and job stability.
Transient Power & Dual-PSU Architecture
How do your configurations handle ATX 3.1 transient power spikes, and why do you utilize dual-PSU architectures?
Multi-GPU arrays—such as dual NVIDIA RTX PRO 6000 Blackwell setups—generate microsecond-long transient power spikes that can double the nominal TDP, tripping the over-current protection (OCP) on standard single-PSU configurations. To prevent this, we split the load across an isolated dual-PSU architecture. A primary 1600W unit (like the be quiet! Dark Power Pro 13) runs the motherboard, CPU, and NVMe drives, while a secondary 1000W unit (like the Corsair SF1000L) is dedicated strictly to auxiliary GPU power rails. This physical separation ensures that massive current draws on the 12Vhpwr / 12V-2x6 lines cannot pull down voltage on the EPS or 24-pin rails, eliminating hard system resets during compute bursts.
8-Channel Memory Throughput
How does an 8-channel memory architecture impact local inference throughput compared to standard consumer platforms?
Standard consumer desktop platforms are physically bottlenecked by dual-channel memory architectures, dropping to ~60–80 GB/s of bandwidth when large models overflow physical VRAM and spill into system RAM. We eliminate this bottleneck by routing workloads through the AMD Threadripper Pro 7995WX on an ASUS PRO WS WRX90E-SAGE SE motherboard. Fully populating all 8 native memory channels with matched Kingston FURY Renegade Pro DDR5 kits yields over 200 GB/s of raw system memory bandwidth. This wider bus maximizes token-per-second performance during hybrid offloading, KV cache swapping, and large embedding executions.
Thermal Mitigation & Chassis Airflow
What specific thermal mitigation strategies prevent thermal throttling in high-density multi-GPU enclosures?
Stacking multiple high-TDP cards in consumer PC cases creates dead air zones, causing the top GPU to choke on the lower card's exhaust and trigger thermal throttling within minutes of continuous compute. We resolve this by using industrial, high-static-pressure enclosures like the Silverstone RM51 4U chassis. The internal layout is calculated around a strict, linear airflow vector that forces high-CFM air directly across the PCIe slots. This continuously evacuates stagnant heat pockets and maintains stable core and VRAM junction temperatures under sustained 24/7 training and local inference workloads.
Facility Infrastructure Requirements
What are the facility power and thermal readiness requirements for these nodes?
Multi-GPU setups have rigorous infrastructure demands. A single high-density node can require dedicated 240V circuits and generate substantial thermal output. Your Certified Fit Audit will explicitly detail the exact kilowatt draw and BTU heat dissipation of your proposed configuration. This allows your facilities team or datacenter provider to validate your power availability and cooling capacity before the build begins.
Enterprise SLAs & Warranties
What deployment support and hardware warranties are included?
All Alon Products infrastructure builds include a standard hardware warranty covering component defects. For Tier 1 and Tier 2 enterprise deployments running mission-critical inference or training workloads, we provide optional operational Service Level Agreements (SLAs). These SLAs guarantee priority remote diagnostics and expedited hardware replacement options directly from our UK engineering lab to minimize compute downtime.
What are the key factors to consider when choosing a compute infrastructure for ML Engineers?
VRAM capacity and memory bandwidth dictate everything. If your model doesn't fit in VRAM, it offloads to the CPU and your inference speed dies. You need 32GB GDDR7 as the baseline. Verify your motherboard supports true PCIe 5.0 x16 lane distribution for future dual-GPU scaling.
How do I optimize my compute infrastructure for 24/7 workloads and ensure sustained structural stability?
Thermal undervolting and operating system choice. Ditch Windows bloat. Run Ubuntu 24.04 LTS or NixOS. Use a 1200W+ Platinum-rated PSU and acoustic-focused chassis to prevent thermal throttling during continuous local fine-tuning runs.
What are the best practices for architecture mission-critical infrastructure for AI and machine learning workloads?
Sovereign Compute. Air-gap your environments. Relying on cloud APIs means a provider can alter model alignment overnight and break your production pipelines without warning. Run your weights locally on Alon Nodes to guarantee 100% uptime and zero data exfiltration.
How much does a bespoke compute infrastructure cost for AI teams and what factors affect the price?
A professional local inference node costs between £5,000 and £8,000. Price scales based on VRAM requirements (e.g., single vs dual RTX 5090) and DDR5 system memory. Alon Nodes are physical CapEx assets. You can claim 100% first-year tax write-offs via RDA (Research and Development Allowances) or AIA (Annual Investment Allowance). We supply the required HMRC Technical Utility Statement with every build.
What are the best options for storing model weights and eliminating storage bottlenecks in compute infrastructure?
PCIe 5.0 NVMe M.2 drives. When swapping 21GB GGUF weights for a 35B model into memory, SATA or older NVMe drives cause massive load latency.
How can I ensure my compute engines remain saturated and bottleneck-free during 24/7 continuous workloads?
The RTX 5090 pushes 1,792 GB/s. Keep the batch size optimal for your VRAM ceiling and ensure your CUDA 12.8 toolkit is path-configured correctly out of the box. If you spill over into system RAM, your engine is no longer saturated; it's crawling.
What are the key differences between high-density compute infrastructure and standard workstations for ML Engineers?
High-density infrastructure is built for continuous tensor calculations. Alon Nodes prioritize PCIe lane availability for multi-GPU clustering, hardened CUDA dependencies, and zero aesthetic RGB bloat.
What is the maximum VRAM capacity for a single node?
For consumer-tier discrete architectures, a single RTX 5090 delivers 32GB GDDR7. You can run dual cards for 64GB in a single chassis. If you need 128GB+ unified memory, you are looking at Apple Silicon, but you sacrifice significant training and fine-tuning speed.
How to solve the problem of creating an engaging mobile app for my business?
The solution to a responsive AI app is serving your own models. Host your inference backend on local Alon Nodes to guarantee sub-second token generation for your mobile clients without paying £800/mo in AWS cloud bills.