llama.cpp Server Hosting in the Netherlands
Running llama.cpp directly (rather than through Ollama) gives you exact control over quantization, speculation, sampling, and grammar constraints. For production LLM deployments serving a public API or backing a SaaS feature, that control matters. AnubizHost ships llama-server on dedicated RTX 4090 in Amsterdam, with AMS-IX peering keeping model pulls fast and API egress effectively free.
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When llama-server Beats Ollama for Production
Ollama is great for personal use. llama-server is the right tool when you need: deterministic KV cache slot management, multi-request batching with explicit slot allocation, grammar-constrained generation for JSON or function calling, speculative decoding with custom draft model, FIM (fill-in-middle) endpoints for coding assistants.
The OpenAI-compatible API is the same shape on both. What changes is the operational knobs available. For production deployments where latency P99 matters, llama-server's slot system gives you predictable behavior.
AMS-IX Bandwidth Profile for LLM API
An LLM API endpoint serves kilobytes per request. A thousand chat completions per hour at 1KB average response is 1MB/hour, dust on a 1Gbps link. The bandwidth case for NL is the model pull side: switching from Llama 3.1 to Qwen 2.5 to Mistral Large means 40-50GB downloads each, which on AMS-IX peered Hugging Face mirrors finishes in 60-90 seconds.
Hardware: 4090 with EPYC Memory Bandwidth
RTX 4090 24GB, EPYC Genoa with 460 GB/s memory bandwidth, 32GB DDR4 ECC, 500GB NVMe, 1Gbps unmetered. The EPYC memory bandwidth matters for CPU-GPU split inference on 70B+ models where some layers offload to system RAM.
For pure-GPU inference of models that fit in 24GB, the GPU does all the work and CPU is mostly idle. For models above 24GB, the EPYC's bandwidth keeps the offloaded layers from becoming a bottleneck.
Speculative Decoding and Throughput
Pre-configured pairs: Llama 3.1 70B with Llama 3.2 1B draft, ~1.8x speedup. Qwen 2.5 72B with Qwen 2.5 1.5B draft, ~1.6x. Mistral Large with Mistral Nemo as draft, ~1.4x.
For coding workloads, Qwen 2.5 Coder 32B paired with Qwen 2.5 Coder 1.5B as draft model achieves ~70 tok/s on the 4090, competitive with hosted Together / Fireworks pricing at fraction of cost when amortized over a month.
Order
$189/mo. Pay crypto. Ready 15-20 minutes. llama-server on localhost, nginx TLS on auto-generated subdomain plus optional .onion. Pre-cached: Llama 3.1 8B/70B, Qwen 2.5 32B/Coder, Mistral Nemo, BGE-large embedding.
Related: AI hosting, Ollama alternative, vLLM for batch throughput, live pricing.
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