How to optimize gpu inference performance for llms

What are the trade-offs between small models and large language models?

While small models (under 3 billion parameters) show extreme speed in benchmarks, they often lack the reasoning capabilities required for complex enterprise tasks. Kog’s initial demonstrations achieved 3,000 tokens per second using a 2-billion parameter model, but the industry demand remains focused on larger models (TechCrunch, 2026). The current challenge for developers is applying these high-speed decoding techniques to larger, more capable LLMs that typically struggle with the memory overhead of inference, without forcing businesses to undertake the complex process of fine-tuning smaller, less capable models.

How do you approach optimizing your own AI inference pipeline?

To improve your inference performance, you must first audit your current hardware utilization and identify where the bottleneck occurs. Follow these steps to optimize your pipeline:

1. Benchmark your current latency: Measure your tokens per second (TPS) across different request volumes to determine if your hardware is being underutilized.
2. Evaluate software acceleration layers: Instead of immediately investing in new hardware, test software-agnostic inference engines—such as those developed by ZML or Kog—that aim to bypass standard software bottlenecks like CUDA overhead.
3. Assess model size vs. speed: Determine if your use case requires a large model (70B+ parameters) or if a highly optimized smaller model can deliver sufficient quality with significantly lower latency.
4. Monitor memory bandwidth: Focus on architectures that maximize memory bandwidth utilization, as this is often the limiting factor in decoding speed rather than pure raw compute power (TechCrunch, 2026).

By focusing on software-side optimization, enterprises can extend the lifecycle of their current datacenter investments while meeting the increasing demand for near-instant AI responses. As the ecosystem matures, expect more tools to emerge that allow for hardware-agnostic acceleration, reducing the reliance on proprietary software stacks.