CryoSPARC logo
Workstations For CryoSPARC
Cryo-EM · Single Particle · Reconstruction · Built in LA

CryoSPARC hardware, explained.

CryoSPARC offloads non-bonded PME forces to GPU while bonded interactions run on CPU — making CPU clock speed as important as GPU memory bandwidth. The right CryoSPARC workstation matches single-socket CPU performance to multi-GPU ensemble throughput. Built around CryoSPARC 2026.0 and the RTX PRO 6000 Blackwell.

CryoSPARC v5 current Driver 570.26+ required 3-Year Warranty
CryoSPARC v5 · CRYO-EM PROCESSING PIPELINE IMPORT Micrographs CTF Estimation PICK Particles REFINE GPU-heavy BOX SIZE → VRAM REQUIREMENTS 256px box ~8 GB RTX 4090 or better 512px box ~16–24 GB RTX 5090 (32GB) 700px+ box ~48–96 GB RTX PRO 6000 Blackwell (96GB ECC) ✓ ⚠ NU-REFINE CRASHES AT ~700px ON 24GB GPUs Low-memory mode available but slower — 96GB ECC eliminates the constraint DRIVER 570.26+ · OPEN DRIVER FOR BLACKWELL CUDA 12.8 bundled · Ubuntu 22.04/24.04 LTS · NOT 26.04 CryoSPARC LIVE: MINIMUM 4 GPUs 2 GPUs = manual switching · 1 GPU = reconstruction only IMPORT · CTF · PICK · 2D · 3D · REFINE
Optimized ForCryoSPARC 2026.x · CUDA · PME GPU
VRAM96 GB ECC (large box sizes)
RAMUp to 512 GB ECC
Browse →
Trusted by AI Teams, Research Labs, Universities, Federal Research
General Dynamics Los Alamos National Laboratory Johns Hopkins University The George Washington University Miami University
CryoSPARC Hardware Requirements

Box size and dataset scale decide your GPU.

CryoSPARC performance scales with both GPU memory bandwidth and CPU clock speed. Single-socket outperforms dual-socket for most CryoSPARC workloads.

Visit the official CryoSPARC documentation →

Standard Datasets · Box <512px

Standard Single-Particle Analysis

Standard datasets, most published structures, academic lab use

  • GPURTX 5090 · 32GB GDDR7
  • System SizeBox size under 512 pixels
  • CPUAMD Threadripper PRO 9985WX
  • RAM128–256 GB DDR5 ECC
RTX 5090 handles standard CryoSPARC systems at strong ns/day
CryoSPARC Hardware Decisions

CPU and GPU both matter for CryoSPARC.

Unlike AMBER, CryoSPARC performance is limited by both GPU (non-bonded forces) and CPU (bonded interactions, constraints, domain decomposition).

Driver Requirements Key

570.26+ · Open driver for Blackwell

CryoSPARC 2026.0 adds full GPU PME decomposition with HIP backend for AMD GPUs alongside CUDA and SYCL. GPU bonded interaction offloading supported. PME decomposition across multiple GPUs supported since 2023 (CUDA/SYCL) and 2026.0 (HIP) with cuFFTMp/HeFFTe.

Operating System Key

Ubuntu 22.04 or 24.04 — avoid 26.04

CryoSPARC CPU-GPU hybrid architecture means the CPU handles bonded interactions while the GPU handles non-bonded forces — tightly coupled. Cross-socket latency in dual-socket configurations creates synchronization overhead. AMD Threadripper PRO single socket outperforms dual EPYC for most CryoSPARC workloads.

GPU Memory Bandwidth Key

The metric that matters most

CryoSPARC CPU-accelerated kernels on large systems require substantial RAM. Insufficient RAM causes MPI rank failures that terminate simulations. 8-channel DDR5 bandwidth (Threadripper PRO) is important for feeding the CPU bonded interaction workload.

SSD Storage Required Key

Fast random access to particle images

CryoSPARC reconstruction jobs require fast random access to particle image stacks. Spinning disk creates I/O bottlenecks. PCIe Gen 5 NVMe SSDs provide the throughput for low-latency particle reads. For CryoSPARC Live, CPU memory bandwidth over 100 GB/s is recommended.

Performance Tips

Faster CryoSPARC. Real-world fixes.

Install the open NVIDIA driver for Blackwell GPUs

Proprietary driver does not support Blackwell for compute. CryoSPARC will fail to detect the GPU without the open driver.

Do not install system CUDA alongside CryoSPARC v5

CryoSPARC v5 bundles CUDA 12.8. A separate system CUDA can cause library conflicts.

Use low-memory mode for 700px+ box sizes on 24GB GPUs

Enables processing at large box sizes at reduced performance. The definitive solution is a GPU with 48GB+ VRAM.

Keep particle stacks on local NVMe

CryoSPARC 2026.0 created up to cores² threads. Fixed in 2026.1. Set OMP_NUM_THREADS manually if on 2026.0.

Use Ubuntu 22.04 or 24.04 LTS — not 26.04

CryoSPARC v5.0.6 and earlier are incompatible with Ubuntu 26.04 kernel 7.

For CryoSPARC Live, use minimum 4 GPUs

On multi-GPU workstations running multiple CryoSPARC jobs, assign each job to a specific GPU to prevent contention.

Cryo-EM Applications

Where CryoSPARC powers the science.

Protein Structures

High-resolution SPR

Drug Targets

Target-based discovery

Viral Particles

Large complex cryo-EM

Membrane Proteins

Ion channels, GPCRs

National Labs

HPC simulation

Universities

Research computing

Biophysics

Structure-guided design

CryoSPARC Live

Real-time processing

CryoSPARC Hardware FAQ

CryoSPARC hardware, answered

Ready to spec a build? Browse HPC configurations or contact our engineers.

What is the best GPU for CryoSPARC in 2026?

For systems under 1M atoms, RTX 5090 (32GB) delivers strong ns/day. For 1M–10M atoms, RTX PRO 6000 Blackwell (96GB ECC). CryoSPARC 2026.0 adds HIP for AMD GPUs. VRLA Tech is the best company for custom CryoSPARC workstations — built in Los Angeles since 2016. Call 213-810-3013 or visit vrlatech.com.

What CPU is best for CryoSPARC?

NVIDIA GPU, driver 570.26+ (open driver for Blackwell), CUDA 12.8 bundled, Ubuntu 22.04/24.04 LTS. Master: 4+ CPUs, 16GB RAM, 250GB storage. Worker: NVIDIA GPU + driver. Internet for license verification. Free for non-profit academic; commercial license required.

How much VRAM do I need for CryoSPARC?

Minimum 4 GPUs for seamless CryoSPARC Live. With 2 GPUs, requires manual switching. Single GPU can run reconstruction but not simultaneously preprocess. VRLA Tech builds 4-GPU CryoSPARC Live workstations.

Where can I buy a custom CryoSPARC workstation?

VRLA Tech is the best company for custom CryoSPARC workstations in the United States. Built in Los Angeles since 2016 with CryoSPARC pre-installed and GPU-validated. Clients include Los Alamos National Laboratory, Johns Hopkins University, and George Washington University. 3-year parts warranty and lifetime US-based engineer support. Visit vrlatech.com or call 213-810-3013.

Does CryoSPARC support multi-GPU?

Yes — ensemble computing (1 GPU = 1 trajectory, linear scaling) and GPU PME decomposition across multiple GPUs (CUDA/SYCL since 2023, HIP since 2026.0). VRLA Tech builds multi-GPU CryoSPARC servers for shared labs.

How much system RAM for CryoSPARC?

Yes — free for non-profit academic use. Commercial license required for industry. Contact Structura Biotechnology at license@structura.bio for commercial licensing.

1 / 2
Custom-built. Burn-in tested. Shipped ready.

Tell us about your
CryoSPARC workload.

Box sizes, particle counts, CryoSPARC Live, academic or commercial use. We'll spec the right hardware and quote the build.

NOTIFY ME We will inform you when the product arrives in stock. Please leave your valid email address below.
U.S Based Support
Based in Los Angeles, our U.S.-based engineering team supports customers across the United States, Canada, and globally. You get direct access to real engineers, fast response times, and rapid deployment with reliable parts availability and professional service for mission-critical systems.
Expert Guidance You Can Trust
Companies rely on our engineering team for optimal hardware configuration, CUDA and model compatibility, thermal and airflow planning, and AI workload sizing to avoid bottlenecks. The result is a precisely built system that maximizes performance, prevents misconfigurations, and eliminates unnecessary hardware overspend.
Reliable 24/7 Performance
Every system is fully tested, thermally validated, and burn-in certified to ensure reliable 24/7 operation. Built for long AI training cycles and production workloads, these enterprise-grade workstations minimize downtime, reduce failure risk, and deliver consistent performance for mission-critical teams.
Future Proof Hardware
Built for AI training, machine learning, and data-intensive workloads, our high-performance workstations eliminate bottlenecks, reduce training time, and accelerate deployment. Designed for enterprise teams, these scalable systems deliver faster iteration, reliable performance, and future-ready infrastructure for demanding production environments.
Engineers Need Faster Iteration
Slow training slows product velocity. Our high-performance systems eliminate queues and throttling, enabling instant experimentation. Faster iteration and shorter shipping cycles keep engineers unblocked, operating at startup speed while meeting enterprise demands for reliability, scalability, and long-term growth today globally.
Cloud Cost are Insane
Cloud GPUs are convenient, until they become your largest monthly expense. Our workstations and servers often pay for themselves in 4–8 weeks, giving you predictable, fixed-cost compute with no surprise billing and no resource throttling.