Llama Server Hosting For Self Hosted Llama Models
Run Llama 3, Llama 3.1, or any fine tuned Llama variant on hardware you control. AnubizHost Llama server hosting includes optional NVIDIA GPU passthrough, large RAM tiers, NVMe storage, and crypto only billing. Serve uncensored or domain tuned Llama models on your own private endpoint with no third party in the inference path.
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Llama Sizes And Hardware Needed
Llama models range from 1B to 405B parameters. The 8B variant fits comfortably on a single GPU with 16GB VRAM or runs CPU only on a 16GB RAM VPS in 4 bit quantization. The 70B variant needs roughly 40GB VRAM in 4 bit or a CPU only deployment with 64GB to 128GB RAM. The 405B variant requires multi GPU or very large RAM dedicated servers.
AnubizHost plans cover the practical range: GPU VPS with RTX 4090 for 8B and 13B class models, GPU dedicated with A5000 or A6000 for 70B inference, and high RAM tiers up to 256GB for slower CPU only serving of the larger variants.
Serving Stack Options
Pick your serving framework: llama.cpp for CPU and GPU offload with GGUF quantizations, vLLM for high throughput GPU serving, Ollama for the easiest install, or text generation inference for production grade deployments. All install cleanly on AnubizHost templates with no platform restrictions.
Front your Llama endpoint with Nginx or Caddy for TLS and authentication, and you have an OpenAI compatible API that costs a fixed monthly fee instead of per token. Many customers use this to power chat clients, RAG pipelines, internal tooling, or commercial products.
No Censorship, No Logs, Crypto Billing
The Llama license permits commercial use within Meta's stated limits. AnubizHost adds no extra filter layer, no prompt logging, and no review of which fine tunes you run. Uncensored variants, abliterated models, and domain specific fine tunes all work the same as the base model.
Billing is crypto only. Pay in Bitcoin or Monero monthly and your Llama server stays online. Offshore jurisdictions in Romania, Iceland, and Finland keep the legal context favorable for private inference workloads.
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