Custom-built high-performance workstation serving dual purposes: gaming and engineering work (CAD, FEA, rendering, machine learning). Designed for demanding applications requiring strong single-thread performance, GPU acceleration, and ample RAM for multitasking.
Primary Uses:
CAD modeling (Siemens NX, SolidWorks, nTopology)
FEA simulations (NX Nastran, Abaqus)
3D rendering and visualization (Blender)
Gaming (high/ultra settings, 1440p)
Machine learning experimentation
Video editing and content creation
General development environment
Compute:
CPU: Intel Core i7-9700K (8-core, 3.6GHz base, 4.9GHz boost)
Unlocked "K" series for overclocking potential
Strong single-thread performance for CAD responsiveness
8 cores for parallel workloads (rendering, simulation)
CPU Cooler: Cooler Master Hyper 212 EVO
Budget tower cooler
Adequate for stock speeds, marginal for heavy overclocking
Memory:
RAM: 64GB Corsair Vengeance LPX DDR4-3200 (4x16GB)
Quad-channel configuration (optimal bandwidth)
3200MHz CL16 (good balance of speed/latency)
64GB essential for:
Large CAD assemblies (PANDORA full robot model)
FEA with fine mesh (millions of elements)
Multiple applications simultaneously
Chrome with 100 tabs (kidding... mostly)
Graphics:
GPU: EVGA RTX 2070 SUPER (8GB GDDR6)
CUDA cores for GPU-accelerated CAD/FEA
Ray tracing capability (visualization)
Tensor cores (AI/ML acceleration)
Sufficient VRAM for 1440p gaming and professional work
Storage:
Boot/Apps: 1TB WD Blue SN5000 NVMe (PCIe 4.0)
Fast OS and application loading
NVMe speed for large file operations
Secondary: 2TB WD Blue SA510 SATA SSD
Game library and project files
Still significantly faster than HDD
Backup: 8TB WD Black HDD
Game library and project files cold storage
Infrastructure:
Motherboard: MSI MPG Z390 Gaming Plus
Z390 chipset (overclocking support)
4x DIMM slots (64GB capacity)
M.2 NVMe slot
Adequate VRM for i7-9700KF
Case: Fractal Design Meshify 2
Excellent airflow (mesh front panel)
Clean aesthetic
Cable management features
Dust filters
PSU: Corsair RM850 (850W, 80+ Gold)
Headroom for GPU power spikes
Fully modular (clean builds)
Quiet operation (semi-passive fan)
Total Cost: ~$1,800
Why i7-9700KF?
For CAD Work:
Single-thread performance critical for CAD responsiveness
9th gen Intel competitive single-core speeds
8 cores sufficient for background tasks while modeling
For Gaming:
High boost clocks (4.9GHz)
No GPU bottleneck with RTX 2070 Super
Future-proof for several years
Why 64GB RAM?
Engineering Justification:
Large FEA models: 8-16GB+ RAM usage
PANDORA full assembly in NX: 6-10GB
Topology optimization: RAM-intensive
Multiple CAD instances + FEA + browser: 20-30GB typical usage
Personal Experience: Upgraded from 32GB after hitting memory limits during FEA convergence studies. 64GB eliminates swapping, maintains responsiveness.
Overkill for gaming? Yes. Necessary for work? Absolutely.
Why RTX 2070 Super?
GPU-Accelerated Workflows:
CUDA acceleration in nTopology (topology optimization)
GPU rendering in visualization tools
Some FEA solvers leverage GPU compute
Future ML experimentation
Gaming Performance:
1440p high/ultra settings (target use case)
Ray tracing capability (growing game support)
Sufficient VRAM (8GB)
Sweet Spot: RTX 2070 Super offered best price/performance in 2019 (pre-GPU shortage). Significant upgrade over GTX series for compute workloads.
Why Fractal Meshify 2?
Airflow Priority:
Gaming + CAD rendering generates heat
Mesh front panel (unrestricted intake)
Multiple fan mounting options
Positive pressure setup (reduces dust)
Build Quality:
Steel construction (rigid, dampens vibration)
Clean aesthetic (no RGB overload)
Excellent cable management
Easy to work in
850W PSU - Overkill?
Power Budget:
i7-9700KF: 125W (TDP) / 180W (max)
RTX 2070 Super: 215W TDP / 250W peaks
System total: ~450W under full load
Headroom Justification:
PSU efficiency curve (most efficient at 50% load)
GPU transient spikes (momentary 300W+)
Future upgrade path (higher-end GPU)
Longevity (less thermal stress on PSU components)
Result: System typically draws 350-450W, PSU operates in efficiency sweet spot, remains quiet.
CAD Modeling (Siemens NX, SolidWorks):
Large assemblies (500+ parts): Smooth rotation, minimal lag
Complex surfaces/topology: Responsive
Rendering previews: Near real-time with GPU
Assembly mates/constraints: Instant updates
FEA (NX Nastran, Abaqus):
Mesh generation: 8 cores significantly faster than 4-core systems
Solver times: Moderate models (100K elements) solve in minutes
Large models (1M+ elements): Benefit from 64GB RAM (no swapping)
Post-processing: Smooth animation of results
3D Rendering (Blender):
GPU-accelerated rendering: Significant speedup vs CPU-only
Real-time viewport performance: High-quality shadows/lighting
Gaming Performance (1440p):
AAA titles: High/Ultra settings, 60-100+ FPS
Esports titles: 144+ FPS (competitive)
Ray tracing: Playable with DLSS
Machine Learning:
TensorFlow/PyTorch: CUDA acceleration functional
Small-to-medium datasets: Trainable locally
Large models: Still benefit from cloud (more VRAM needed)
General Productivity:
Multiple applications simultaneously (CAD + FEA + browser + Slack)
No slowdowns or stuttering
Fast boot times (NVMe)
Instant application launches