How to Run Llama 3 / Llama 4 on a Dedicated GPU Server (Ollama + vLLM Guide)
I’ve set up Llama models on more hardware configurations than I’d like to admit, and the gap between “it runs” and “it runs well” almost always comes down to one thing: whether you’re on a proper dedicated GPU server or trying to make do with something shared. Llama 3 and Llama 4 are capable models, but they’re not forgiving about half-measures on infrastructure.
This guide walks through running Llama 3 and Llama 4 with both Ollama and vLLM, and where each one actually makes sense.
Why the Hardware Choice Comes Before the Software Choice
People jump straight to “Ollama or vLLM?” before asking the more basic question: what am I running this on? A dedicated GPU server gives you the full GPU, predictable memory, and no other tenant’s workload spiking your latency mid-inference. Skip that step and the rest of this guide won’t matter much — both Ollama and vLLM will underperform on shared or undersized hardware regardless of how well you configure them. GPU4Host Llama 3 server hardware requirements are a reasonable starting reference if you want concrete numbers instead of vague rules of thumb. Generally:- Llama 3 8B runs comfortably on 16-24GB VRAM
- Llama 3 70B needs 80GB+ VRAM (or multi-GPU setups)
- Llama 4 variants, depending on size, can need even more headroom for context length
Ollama: The Faster Path to “It Works”
Ollama is the easier on-ramp. If you want Llama 3 running locally or on a server in under ten minutes, this is it. bash curl -fsSL https://ollama.com/install.sh | sh ollama run llama3 That’s genuinely most of it. Ollama handles quantization, model pulling, and a basic API server out of the box. For a UK GPU dedicated server Ollama Llama 3 setup, this is usually the first thing teams try before deciding whether they need something heavier. The catch: Ollama isn’t built for high concurrency. It’s great for prototyping, internal tools, or single-user workloads. Once you need to serve dozens or hundreds of concurrent requests, it starts to strain.vLLM: Where Production Workloads Actually Live
vLLM is the better choice once you’re serving real traffic. It uses PagedAttention to manage memory far more efficiently, which means higher throughput on the same dedicated GPU server hardware. bash pip install vllm python -m vllm.entrypoints.openai.api_compatible_server \ –model meta-llama/Meta-Llama-3-8B \ –tensor-parallel-size 1 This spins up an OpenAI-compatible API, which makes swapping it into existing applications fairly painless. For a Netherlands dedicated GPU Llama 3 vLLM production setup, this is the standard pattern — vLLM handling concurrent requests, batching them efficiently instead of processing one at a time. If you’re running Llama 4’s larger variants, tensor parallelism across multiple GPUs becomes necessary, and that’s exactly where a real dedicated GPU server earns its cost over a shared instance — you’re not fighting for PCIe bandwidth with someone else’s job.Choosing Your Region: It’s Not Just About Latency
Where you host this matters more than people initially assume.- A Germany GPU server vLLM Llama 4 EU deployment is common for teams that need EU data residency without sacrificing throughput — Germany’s connectivity across the continent makes it a solid default.
- For lighter, self-hosted experimentation, a France GPU node self-hosted Llama inference setup tends to work well, especially for teams already running infrastructure elsewhere in the EU.
- If your use case is latency-sensitive — voice assistants, real-time chat — a Sweden GPU server Llama 4 low-latency inference configuration is worth considering, given the strength of Nordic network infrastructure.
- For genuinely sensitive deployments, a Switzerland GPU server Llama air-gapped deployment is the strictest option. Swiss hosting law plus the option for fully isolated, non-internet-connected inference makes this the choice for legal, healthcare, or defense-adjacent use cases.
- An Ireland GPU server EU Llama model hosting setup is common too, partly for proximity to other EU infrastructure and partly because it’s a well-trodden path for companies bridging US and EU operations.
- If you’re cost-conscious, India GPU cloud Ollama Llama 4 self-hosted setup options are genuinely competitive — you get real GPU capacity without West European or US pricing, which matters while you’re still validating whether self-hosting is worth it at all.
- And for raw throughput, a USA GPU server Llama 4 high-throughput serving setup remains the default for teams running the largest models at scale, since the newest GPU generations land in the US first.
Where Infinitive Host Fits
If you’re comparing providers for this, Infinitive Host is worth shortlisting — particularly if you want a dedicated GPU server with region flexibility rather than being locked into one data center. There’s an active InfinitiveHost Llama GPU plans — get 25% OFF offer right now, which is a reasonable time to lock in pricing if you were already planning to deploy Llama 3 or Llama 4 this quarter. That said, benchmark your actual model and concurrency needs first — a discount won’t help if you end up on the wrong GPU tier and have to migrate later.Practical Notes Before You Deploy
- Start with Ollama to validate the model fits your use case, then move to vLLM once you need real concurrency.
- Match VRAM to your actual model size and context length, not the smallest number that technically loads the model.
- If compliance matters, decide on region before deployment — Germany or Switzerland save you trouble later.
- Talk to GPU4Host or Infinitive Host about your specific Llama variant before committing to a long-term plan.
