By VRLA Tech  ·  Case Study  ·  June 2026

A researcher needed a single full-tower workstation covering a broad set of requirements: good GROMACS molecular dynamics performance, support for three or more 4K monitors, tensor AI computing, financial time series analysis, dual-boot Windows and Linux from independent drives, and a chassis and PSU with room to add a second GPU within two years. This is how VRLA Tech specced and built that system.

The requirements: one machine, six distinct demands

The customer’s requirements spanned several domains that do not always map to the same hardware choices. Working through each one:

GROMACS performance — CPU/GPU balance matters

GROMACS is a CUDA-accelerated molecular dynamics package that offloads nonbonded force calculations, PME electrostatics, bonded interactions, and coordinate updates to the GPU. However, GROMACS keeps the CPU active at every timestep — handling task management, communication, and remaining force computations in parallel with the GPU. This means CPU core count directly gates simulation throughput. Benchmarks from the GROMACS community recommend 16 to 32 modern CPU cores per GPU for optimal CPU/GPU balance on Blackwell-architecture cards. An underpowered CPU creates a bottleneck that prevents the GPU from reaching its full ns/day potential regardless of GPU capability.

Three or more 4K monitors

The RTX 5080 has three DisplayPort 2.1b outputs and one HDMI 2.1b output — four total display outputs, each capable of driving 4K at up to 480Hz. This precisely matches the requirement for three 4K DisplayPort monitors plus one HDMI connection available for a fourth display or projector.

Tensor AI computing and financial time series analysis

Both workloads run well on the RTX 5080’s 336 fifth-generation Tensor Cores and 16GB GDDR7 at 960 GB/s bandwidth. Financial time series models — whether LSTM, transformer-based forecasting, or quantitative signal processing — are well within the RTX 5080’s compute envelope at 16GB VRAM. These workloads run on Linux in PyTorch or similar frameworks and integrate naturally with the same CUDA environment used for GROMACS.

Dual-boot Windows and Linux from independent drives

VRLA Tech configures this as two independent NVMe OS drives with BIOS-level boot selection between them. Linux runs GROMACS, PyTorch, Python scientific computing, and CUDA development. Windows handles data visualization tools, financial platforms, browser-intensive workflows, and general productivity. The two OSes are completely independent — no virtual machine overhead, no partition sharing, no risk of one OS affecting the other’s configuration.

Future second GPU expansion

The RTX 5080 draws 360W TDP. A full tower chassis with a 1600W PSU provides sufficient headroom to add a second RTX 5080 or equivalent GPU within two years without replacing the power supply. The second GPU would run independently over PCIe 5.0 x16 — the RTX 5080 does not support NVLink, so multi-GPU GROMACS runs use GROMACS’s own GPU-aware MPI decomposition rather than unified VRAM pooling.

10–20 Chrome tabs with video and live data feeds

This is a CPU and system RAM workload, not a GPU workload. Chrome allocates roughly 100–300MB of RAM per active tab with video. 20 tabs with live data feeds and video represents approximately 3–6GB of RAM in browser processes alone, plus OS and application overhead. 64GB or more of DDR5 system RAM is sufficient to run this alongside GROMACS, PyTorch, and development environments without memory pressure.

The build: what VRLA Tech configured and why

Why Ryzen 9 9950X

The 9950X provides 16 Zen 5 cores and 32 threads — the right CPU sizing for a single RTX 5080 GROMACS rig. VRLA Tech builds the AMD Ryzen Workstation on this platform for high-frequency professional and scientific computing workloads. GROMACS community benchmarks consistently show that under-sizing CPU core count relative to GPU count creates ns/day penalties as the CPU fails to keep the GPU fed with work at each timestep. 16 high-frequency Zen 5 cores running at up to 5.7GHz provide strong single-threaded Python and data analysis performance alongside the multi-threaded GROMACS CPU workload. The AM5 platform supports DDR5 and PCIe 5.0, ensuring both the GPU and NVMe storage run at full bandwidth.

Why NVIDIA GeForce RTX 5080

The RTX 5080 is the right GPU for this build for three compounding reasons. First, its display output configuration — 3× DisplayPort 2.1b and 1× HDMI 2.1b — precisely satisfies the three 4K monitor requirement without workarounds. Second, its 16GB GDDR7 at 960 GB/s bandwidth handles GROMACS GPU offloading and financial/AI tensor workloads well within its compute envelope. Third, its 360W TDP in a 1200W+ PSU full tower leaves 840W of headroom for a second GPU — meaning the customer can add a second RTX 5080 (or equivalent) within two years without PSU replacement. At 16GB VRAM, the RTX 5080 is well-matched to the stated AI workloads: tensor computing, financial time series model training, and general PyTorch development at model sizes that fit comfortably within 16GB.

Why 64GB DDR5

64GB exceeds the stated 32GB minimum and is the right choice for this combined workload. GROMACS trajectory data, active Python analysis environments, 20 Chrome tabs with live video feeds, and background OS processes together consume memory that makes 32GB tight in practice. 64GB provides comfortable headroom across all workloads running simultaneously, and the AM5 platform supports straightforward upgrade to 128GB if needed as workloads grow.

Dual NVMe OS drives — Windows and Linux

Two independent NVMe drives are configured with separate OS installations. The BIOS is configured to select between them at boot. This is a cleaner and more reliable dual-OS solution than shared-partition dual-boot: each OS has its own bootloader, file system, and driver environment. There is no risk of a Windows update overwriting the Linux bootloader, and no shared partition that either OS can corrupt. VRLA Tech verifies both operating systems boot cleanly and the BIOS boot selection works correctly before shipping.

Full tower chassis with 1600W PSU

A full tower chassis provides the physical space for a second full-length GPU card and the airflow to cool two GPUs under sustained GROMACS load. Two RTX 5080s at 360W TDP each total 720W for GPUs alone — add the 9950X at up to 170W, motherboard, RAM, storage, and cooling and the system draws approximately 950–1,000W under sustained load, before accounting for transient power spikes above rated TDP. A 1600W ATX 3.1 PSU keeps the system well within the 50–80% efficiency load range at current single-GPU load, and provides safe headroom for dual-GPU operation when the second card is added.

Burn-in testing and delivery

The system was burn-in tested for 48 hours at VRLA Tech’s Los Angeles facility under sustained GPU and CPU load. Both OS drives were confirmed to boot correctly and the BIOS dual-boot selection was verified. The customer received a ready-to-use system.

What this build is optimized for

• GROMACS molecular dynamics simulations with CUDA GPU offloading
• Tensor AI computing — PyTorch model training and inference on 16GB GDDR7
• Financial time series analysis — LSTM, transformer forecasting, quantitative signal models
• Three or four simultaneous 4K monitor workflows
• Dual-boot Windows and Linux from independent NVMe drives
• Heavy browser workloads — 10–20 Chrome tabs with live video and data feeds
• Python scientific computing — NumPy, SciPy, pandas, Jupyter
• Future dual-GPU expansion without chassis or PSU replacement

Built and configured by the VRLA Tech engineering team in Los Angeles. VRLA Tech has been building custom AI workstations and GPU servers for research, enterprise, and scientific computing teams since 2016.