High-Performance GPU Platforms for RELION Cryo-EM
Purpose-built computational nodes engineered for RELION. Accelerate your entire single-particle analysis pipeline—from beam-induced motion correction and CTF estimation to Bayesian polishing and high-resolution 3D auto-refinement.
RELION Software Ecosystem & Deployment Support
Every RELION workstation ships with OS installed, NVIDIA CUDA toolkits validated, and GPU drivers configured. Our engineers verify solver execution and run benchmark reconstruction pipelines before handover.
We Validate Your RELION Environment
Skip the complex MPI and dependency configurations. Kirti Telnet provisions your hardware with validated RELION 5.x binaries, CTFFIND4, MotionCor2, and optimized CUDA/OpenMPI runtimes so you can begin processing micrographs immediately.
Primary Pipeline
Bayesian Single Particle Analysis
Best for: High-Resolution 3D Reconstruction & Classification
Preprocessing & Picking
Micrograph Alignment & AI Particle Picking
Best for: Automated Particle Extraction & CTF Correction
CTFFIND4 — Contrast transfer function estimation and correction
Topaz / crYOLO — Deep learning-based automated particle picking
Visualization & Modeling
Map Fitting & Atomic Model Building
Best for: Interactive 3D Structure Visualization
Coot — Interactive atomic model building into electron density maps
Phenix — Automated real-space refinement and validation
Supported Operating Systems
HPC & Core Facility Compliance
Best for: Stable HPC & Research Environments
Ubuntu LTS — Preferred Linux for CUDA development & RELION deployment
Enterprise System Matrix — RELION Cryo-EM Platforms
RELION requires a strict balance of resources. While multiple GPUs handle the heavy 2D/3D classification, Bayesian polishing and preprocessing are extremely demanding on system memory (RAM) and storage I/O. Our systems are balanced to ensure no component starves the pipeline.
All prices are tentative and subject to change based on configuration and component availability. RELION is free, open-source software developed at MRC Laboratory of Molecular Biology.
Entry Cryo-EM Node
Initial 2D classification, CTF estimation, and introductory single-particle refinement on moderate-resolution datasets with CUDA acceleration.
RAM: 128 GB DDR5
GPU: 1x NVIDIA RTX 4000 Ada 20GB
Storage: 1 TB Gen4 NVMe
Starting Price
Advanced Processing Workstation
Mid-complexity 3D auto-refinement, MotionCor2 preprocessing, and Topaz-based AI particle picking with ECC-validated memory for data integrity.
RAM: 256 GB DDR5 ECC (4-Channel)
GPU: 1x NVIDIA RTX 4500 Ada 24GB
Storage: 2 TB Gen4 NVMe
Starting Price
Heavy Classification Rig
Multi-class 3D classification, Bayesian polishing with per-particle motion estimation, and dual-GPU MPI acceleration with 8-channel ECC memory bandwidth.
RAM: 512 GB DDR5 ECC (8-Channel)
GPU: 2x NVIDIA RTX 5000 Ada 32GB
Storage: 2 TB Gen5 NVMe + 2 TB Scratch
Starting Price
Extreme Reconstruction Rig
Sub-angstrom 3D auto-refinement, quad-GPU MPI classification, and 1TB system RAM for massive particle datasets with 48GB VRAM per GPU for high-resolution polishing.
RAM: 1 TB DDR5 ECC (8-Channel)
GPU: 4x NVIDIA RTX 6000 Ada 48GB
Storage: 4 TB Enterprise NVMe RAID
Starting Price
Enterprise RELION Cluster Node
Multi-node MPI clusters for high-throughput cryo-EM facilities, enterprise job scheduling with InfiniBand interconnects, and 1.5TB RAM for continuous large-scale data collection processing.
RAM: 1.5 TB DDR5 ECC Reg (12-Channel)
GPU: 4x NVIDIA L40S 48GB
Storage: 7.68 TB Enterprise U.2 NVMe
Starting Price
Engineering Disclaimer: RELION performance depends on the balance between GPU VRAM, system RAM, and NVMe I/O speed. Bayesian polishing is highly RAM-intensive; we recommend a minimum of 64–128 GB of system RAM per installed GPU. Multi-node clusters require InfiniBand networking for efficient MPI scaling.
Architectural Features & Performance
The I/O Bottleneck in Bayesian Polishing
Bayesian polishing in RELION reads and writes massive amounts of individual particle data. Standard hard drives cause the pipeline to stall. We integrate PCIe Gen 5 NVMe scratch arrays to ensure storage I/O speeds never throttle your CPU or GPU processing times.
RAM-to-GPU Memory Ratio
RELION requires a strict ratio of system RAM to GPU VRAM to prevent out-of-memory crashes during 3D refinement and polishing. We configure our workstations with a minimum of 64GB to 128GB of DDR5 ECC memory per installed GPU, ensuring seamless data staging.
Linear Multi-GPU Scaling
RELION’s 2D and 3D classification tasks scale exceptionally well across multiple GPUs using MPI. Our architectures utilize 2 to 4 NVIDIA RTX Ada Generation GPUs per node to drastically reduce the time-to-insight for massive datasets.
Frequently Asked Questions
How much RAM do I need for RELION?
While 128GB is the starting point, steps like Bayesian polishing are incredibly RAM-intensive. We recommend at least 64GB to 128GB of system RAM per installed GPU to ensure smooth data processing without paging to the disk.
Do I need a fast SSD for RELION?
Absolutely. Cryo-EM movies are massive (often multi-terabyte datasets). Processing these files during motion correction and particle extraction requires enterprise-grade NVMe SSDs configured as dedicated scratch drives to prevent severe I/O bottlenecks.
What GPUs are best for RELION?
High-VRAM GPUs like the NVIDIA RTX 4500 Ada (24GB), 5000 Ada (32GB), or 6000 Ada (48GB) are ideal. They provide the memory necessary to process large particle box sizes and the CUDA cores to accelerate 3D classification and refinement.
Does Kirti Telnet provide RELION cluster setups?
Yes. For high-throughput cryo-EM facilities, we design distributed MPI clusters with high-speed InfiniBand interconnects and parallel storage fabrics, allowing you to queue and process massive data collections seamlessly.
Request Custom Cryo-EM Architecture
Talk to KTPL’s infrastructure engineers for a workload assessment and GPU-optimised hardware recommendation for your RELION cryo-EM processing environment.
Get in Touch Today for Expert Solutions and Support