VLLM

ROCm and Vulkan both accelerate AMD GPUs for local LLM hosting, but they are not interchangeable. The right choice depends on the engine, GPU, and workload.

A model can advertise a 128K context window and still fail at 40K tokens on a 16 GB GPU. The architecture ceiling never promised that weights, KV cache, compute buffers, and the desktop compositor would fit on your card at the same time.

Ollama is one of the easiest ways to run a local language model, but convenience can conceal the moment when a local experiment becomes a shared inference service that needs better scheduling and observability.

LLM inference looks like “just another API” — until latency spikes, queues back up, and your GPUs sit at 95% memory with no obvious explanation.

Strategic guide to hosting large language models locally with Ollama, llama.cpp, vLLM, or in the cloud. Compare tools, performance trade-offs, and cost considerations.

vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs) developed by UC Berkeley’s Sky Computing Lab.

Running LLMs locally is now practical for developers, startups, and even enterprise teams.
But choosing the right tool — Ollama, vLLM, LM Studio, LocalAI or others — depends on your goals: