An overview of local LLM inference bottlenecks, quantization formats, Ollama's role and limits, and vLLM optimizations such as PagedAttention and continuous batching.
Who This Is For
Engineers moving from local LLM experiments to higher-throughput inference systems.
Summary
This article explains the path from simple local LLM experiments to more serious inference optimization. It compares the role of Ollama with the performance goals of vLLM, and explains why memory layout, batching, quantization, and GPU utilization become important once traffic and latency matter.
Key Takeaways
- Ollama is useful for quick local experimentation but has limits under heavier concurrency.
- vLLM improves serving efficiency through PagedAttention and continuous batching.
- Quantization choices affect memory footprint, speed, compatibility, and quality.
- Inference optimization is a system problem involving model format, scheduler behavior, and hardware constraints.
Implementation Focus
- Start with local experimentation, then identify the bottleneck: model loading, batching, KV cache, GPU memory, or request scheduling.
- Choose quantization formats by balancing quality, memory budget, serving framework support, and latency goals.
- Move from single-user local tools to serving engines when concurrency, throughput, and predictable latency matter.
Practical Use Cases
- Deciding when Ollama is enough and when vLLM is a better serving layer.
- Planning local model serving for AI tools, demos, or internal productivity agents.
- Explaining inference performance tradeoffs to engineers new to LLM infrastructure.
Why It Matters
This gives international readers a practical view of how the author thinks about LLM infrastructure, not just application-layer Agents.