High-Performance AI Server Build 2026: RTX 5090 + Core Ultra 9 285K

~$7,520 Complete Build | LLM Inference: 200–400 tokens/sec on 7B | 70B models: Yes, quantized | Stable Diffusion XL: 12+ it/s | VRAM: 32GB GDDR7

If the budget AI workstation is the right tool for learning and light production work, this is what you build when the 12GB ceiling starts showing up in your error logs. The RTX 5090's 32GB GDDR7 is the first consumer GPU that comfortably runs 70B parameter models in meaningful quantizations — not as a party trick, but as a working machine.

This is a dedicated local inference server: runs privately, stays on-premise, serves your network, and doesn't bill per token.

Why 32GB VRAM Changes the Equation

The jump from 12GB to 32GB isn't incremental — it's a different category of capability:

Running a 70B model fully in VRAM vs. partially offloaded to system RAM is a 10–20× inference speed difference. This is the machine where Llama 3.1 70B becomes usable in real time.

Performance Benchmarks

LLM Inference (Ollama / llama.cpp with GPU offload)

• Llama 3.1 8B (Q4KM): ~350 tokens/sec
• Llama 3.1 70B (Q4KM): ~45 tokens/sec (fully GPU offloaded)
• Mistral 7B (Q8): ~280 tokens/sec
• Multi-user concurrent: 2–4 simultaneous sessions at 7B without queue buildup

Stable Diffusion

• SD 1.5 (512×512, 20 steps): 18 it/s
• SDXL (1024×1024, 20 steps): 12 it/s
• FLUX.1 [dev]: 5–7 it/s

Workstation / Creative

• Blender BMW benchmark: ~55 seconds (GPU render)
• DaVinci Resolve 4K export: Real-time + (CUDA acceleration)
• Cinebench 2026 (CPU): ~2,800 nT

Complete Parts List

Total: ~$7,520 (prices verified April 12, 2026 — significant increases in DDR5 and NVMe storage since early 2026)

RTX 5090 pricing reality (April 2026): MSRP is $1,999 for the Founders Edition, which remains effectively unavailable at retail. AIB cards from MSI, ZOTAC, and ASUS are currently running $3,800–$4,200 — down slightly from the $4,100–$4,500 peak earlier in the year, but still well above MSRP. DDR5 64GB kits and high-end NVMe storage have also spiked significantly since late 2025. Budget accordingly, and use the live Amazon search links for current pricing before purchasing.

Affiliate links: We use Amazon search links here until direct product links are verified. Use the search links to find current pricing and availability.

Build Rationale: Part by Part

CPU: Core Ultra 9 285K (Arrow Lake, LGA1851)

The 285K's 24-core configuration matters here for two reasons: multi-user inference uses CPU threads for tokenization and context management, and the workstation use case (compilation, rendering, batch processing) benefits from core count. Arrow Lake also brings improved memory bandwidth through the Z890 platform — important when model data moves between system RAM and VRAM.

Intel Application Optimization (APO) should be enabled in BIOS for improved thread routing on heterogeneous workloads.

Motherboard: ASUS ROG Maximus Z890 Extreme

The Z890 Extreme earns its price for a server-grade use case:
- PCIe 5.0 x16 for the RTX 5090 — full bandwidth, no bottleneck
- PCIe 5.0 M.2 for the storage — critical for fast model loading (2TB of models loads in seconds)
- 10GbE LAN — essential if this machine serves inference requests over your local network
- Thunderbolt 4 — flexible for peripherals and external storage
- Robust VRM — sustained all-core load needs stable power delivery

If the Extreme is over budget, the ROG Maximus Z890 Hero (~$599) retains the PCIe 5.0 lanes and most connectivity. The main sacrifice is 10GbE (Hero has 2.5GbE).

RAM: 64GB DDR5-6000 CL30

For AI inference, system RAM is the fallback when VRAM overflows. 64GB at high bandwidth ensures:
- 70B Q8 models run with minimal CPU offloading delay
- Multiple models can be loaded in RAM simultaneously for near-instant switching
- LM Studio / Ollama can manage model caches without hitting the floor

DDR5-6000 CL30 is the optimized sweet spot for Z890 — native ratio for the memory controller, tight latency, no overclocking required.

GPU: RTX 5090 (Blackwell, 32GB GDDR7)

This is the whole reason for the build. Key specs for AI work:
- 32GB GDDR7: ~1.8TB/s memory bandwidth — the single most important number for inference speed
- CUDA cores: 21,760 (Blackwell architecture)
- Tensor cores: 4th-gen for transformer operations
- NVLink: Available for future multi-GPU configurations
- TDP: 575W — plan your power accordingly

The bandwidth improvement over previous-gen (1.8TB/s vs. 1.0TB/s on RTX 4090) translates almost linearly to inference throughput.

PSU: Corsair HX1500i 1500W

Non-negotiable for this build. The RTX 5090's 575W TDP plus the 285K's 250W package power means sustained load can approach 950W at the wall. The HX1500i provides:
- 1500W headroom — 35%+ above peak draw
- 80+ Platinum efficiency — lower heat, lower electricity bill over 24/7 operation
- Digital telemetry via iCUE — monitor real-time power draw

Case: Corsair 7000D Airflow

Full tower gives you room for the RTX 5090 (336mm reference length), 360mm AIO, and future expansion. The 7000D's airflow-first design keeps the GPU cool under sustained inference loads — important when you're running continuous workloads, not just gaming bursts.

Three front intake fans + two top exhaust included. Excellent cable management for the inevitable 12VHPWR octopus.

Software Stack

Inference Runtimes

Ollama (recommended for most users)

curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.1:70b
ollama serve  # exposes REST API at localhost:11434

LM Studio — GUI-based model management with OpenAI-compatible API endpoint. Excellent for model browsing, downloading, and switching. Exposes the same API format as OpenAI, making it a drop-in for any OpenAI-compatible application.

llama.cpp — For maximum control and benchmarking. Supports CUDA offloading layer-by-layer, useful for understanding model behavior at memory limits.

Frontend: OpenWebUI

docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

OpenWebUI gives you a ChatGPT-style interface for any Ollama or OpenAI-compatible backend. Access it from any device on your network at http://[server-ip]:3000. Multiple users, conversation history, model switching — it's the full stack for a private AI deployment.

NVIDIA Container Toolkit (for Docker-based workflows)

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list \
  | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit

Enables GPU passthrough in Docker containers — needed for containerized inference services or running multiple isolated model environments.

Recommended Models by Use Case

Power and Thermal Considerations

This is a 24/7 machine. Budget accordingly:
- Sustained inference load: ~800–900W at the wall
- Annual electricity cost (24/7 at $0.13/kWh): ~$900–$1,020/year at full load — in practice closer to $400–$600 with mixed idle/active use
- Case thermals: The 7000D handles heat well. Ensure ambient room temperature stays under 25°C for sustained operation
- GPU junction temp at load: Expect 75–83°C under sustained inference — normal for Blackwell

Serving Your Local Network

To expose inference to other devices:

# Ollama — bind to all interfaces
OLLAMA_HOST=0.0.0.0 ollama serve

# OpenWebUI — already network-accessible via the Docker run command above

With 10GbE from the Z890 Extreme and a 32GB VRAM card, this machine can serve 3–5 simultaneous users on 7B models without perceptible slowdown. For larger models (70B), queue requests — concurrent generation on a 70B model degrades quality per session.

Comparison: Budget AI Workstation vs. This Build

Start with the budget build. If you hit the VRAM ceiling regularly or need 70B quality, the upgrade path is clear.