November 2025 Summaries

Scaling inference is a critical challenge for AI startups, influencing product speed, customer experience, and economic viability. Early infrastructure decisions can lead to technical debt, causing issues as startups scale and necessitating costly rework. The article discusses five approaches to building an inference stack, explaining how each fits into different stages of a startup's journey, from model API endpoints for rapid deployment to multi-cloud and hybrid platforms for scalability and compliance. Inference affects key aspects such as speed, cost efficiency, deployment efficiency, and compliance, all of which are vital for startup success. The article emphasizes that as startups progress from MVP to enterprise, their inference needs evolve, requiring different tools and strategies to maintain efficiency and scalability. It highlights the importance of choosing appropriate inference solutions to avoid infrastructure rebuilds and maintain engineering velocity, advocating for the Bento Inference Platform as a scalable solution for startups transitioning from early-stage tools to production-grade, multi-cloud inference environments.

Loading a large language model (LLM) onto GPUs like the NVIDIA A100 often reveals unexpected challenges with VRAM usage during inference, leading to memory spikes and Out of Memory (OOM) errors. This occurs because the GPU memory dynamically accommodates not only the model weights but also a growing Key-Value (KV) cache that consumes significant resources, especially with long context windows and multi-turn interactions. The text explains the difference between dedicated VRAM and shared GPU memory, emphasizing the importance of understanding GPU memory requirements for LLMs, particularly the impact of the KV cache. Various strategies, such as quantization and distributed inference techniques, are proposed to optimize memory usage, though they require complex engineering. The Bento Inference Platform offers a solution by integrating these optimizations out-of-the-box, allowing AI teams to efficiently run LLMs without the need for extensive infrastructure management.

Open-source models like DeepSeek-R1 and gpt-oss allow users to self-host powerful reasoning models, offering more control and cost-efficiency compared to closed-source APIs. By using frameworks like vLLM and tools like BentoML, developers can create private inference APIs with customizable inference logic and optimized performance through techniques such as prefill–decode disaggregation. The article guides users through self-hosting gpt-oss using vLLM and BentoML, highlighting the benefits of deploying on BentoCloud, a managed inference platform with features like fast autoscaling and LLM-specific observability. vLLM, developed by UC Berkeley researchers, is noted for its high-performance capabilities, making it an ideal choice for handling large language models (LLMs). The deployment process includes setting up a virtual environment, defining model and GPU configurations, configuring runtime environments, and launching a vLLM server within a BentoML service. The article further explains how to deploy gpt-oss to BentoCloud, test OpenAI-compatible APIs, and optimize deployments with scale-to-zero capabilities, while also providing insights into the benefits of BentoML and vLLM, including their ability to efficiently handle large-scale production environments.