Budget Dedicated Cores for Ollama & Vector Search
🏆 Our pick: Hetzner Cloud · 💰 Best value (dedicated): InterServer Dedicated · 🥈 Runner-up: netcup Root Server
Run quantized LLMs (7B Q4) and pgvector/Qdrant on 8–16GB RAM dedicated servers with fast NVMe. GPU not mandatory for RAG demos.
Prices & hardware specs verified 2026-09-03
Start on dedicated: guaranteed cores for this workload (Top 4)
#1 InterServer Dedicated Ryzen 9900X 12 vCPU · 64 GB RAM · configurable disk · unmetered bandwidth $149.00/mo calculated 364.6 CPU Mark/$ Visit ↗
#2 netcup Root Server RS 2000 G12 8 vCPU · 16 GB RAM · 512 GB disk · unmetered bandwidth $21.00/mo calculated 316.7 CPU Mark/$ Visit ↗
#3 Hetzner Dedicated EX63 20 vCPU · 64 GB RAM · 2048 GB disk · unmetered bandwidth $173.73/mo calculated 285.9 CPU Mark/$ Visit ↗
#4 OVHcloud Advance ADVANCE-3 12 vCPU · 64 GB RAM · configurable disk · unmetered bandwidth $233.19/mo calculated 203.8 CPU Mark/$ Visit ↗
Our Recommendations
🏆 Best Fit Hetzner Cloud
CCX23 (4vCPU/16GB) at €86.49/mo ($100.85 equivalent): exact-fit dedicated cores, green EU.
🥈 Most RAM per $ netcup Root Server
RS 8000 G12 (16vCPU/64GB) at €71.36/mo incl. VAT ($69.92 VAT-stripped), cost-effective for large contexts.
🥉 US Alternative BuyVM
KVM Slice 16GB (8vCPU/16GB) at $60/mo for US-side inference.
Key Takeaways
- Ollama 7B Q4 needs ~5GB + OS, so 8GB is the floor.
- Store embeddings on NVMe: SATA tanks HNSW latency.
Gotchas
- No GPU on pure CPU plans, so latency is ~5x vs a dedicated GPU box.
- Sustained inference pins cores at 100%, which is exactly what dedicated billing covers.
Test your own workload
Adjust vCPU, RAM and bandwidth in the calculator.