Nvidia just paid $20 billion for Groq’s inference technology in what is the semiconductor giant’s largest deal ever. The question is: Why would the company that already dominates AI training pay this much for an inference startup?

Because training was the first phase. Inference is now scaling rapidly and becoming the dominant cost for AI companies.

Every ChatGPT query, every AI agent action, every generated video is based on inference. Training a model is a one-time capital expense. Serving it is the recurring operational cost that scales with every user, every request, every second of uptime.

Over the past few years, I have worked with AI infrastructure companies across very different industries: a leading video generation lab, a frontier world-models company, a pharmaceutical company building internal AI tooling and a space technology company using large language models (LLMs) for code security. Despite their differences, they all hit the same four challenges when scaling inference.

1. Picking The Right Hardware For Your Workload

Hardware choice is not just about getting GPUs—it is about matching chip architecture to your model type and latency requirements.

Nvidia dominates the world of GPUs, with roughly 90% market share. Its H100 and B200 chips are the default choice, and for good reason: The CUDA ecosystem is mature, tooling is abundant and engineers know how to work with them. But defaults are not always optimal.

• If you are serving LLMs and latency is critical, Groq’s LPU architecture is purpose-built for speed. This is exactly what Nvidia just acquired.

• If your models are memory-heavy with large context windows, AMD’s MI300X offers 192GB of HBM, which is 2.4 times the memory capacity of Nvidia’s H100.

• If you have high-throughput workloads where latency tolerance is higher, AWS Inferentia and Google TPU can reduce cost per token.

Match chip to workload. Do not default to flagship GPUs when your latency and throughput requirements do not demand them.

2. Choosing The Right Inference Server

Your inference server choice depends on model type, target hardware and how much customization you need.

• If you are serving LLMs on Nvidia and want the fastest path to production, vLLM and SGLang offer continuous batching. PagedAttention can be used for KV cache management, while OpenAI-compatible APIs can be used out of the box.

• If you need maximum optimization on Nvidia hardware, TensorRT-LLM provides FP8 and INT8 quantization with multi-GPU parallelism.

• If you are running LLMs across multiple hardware backends like Nvidia, AMD and TPU, vLLM has the broadest support.

• If you are serving video, audio or image generation, there is no vLLM equivalent. You will likely build a custom stack on Triton Inference Server.

3. Orchestrating AI Containers At Scale

Everyone uses Kubernetes for inference orchestration. But vanilla K8s was not built for AI workloads, and the gaps become painful at scale.

Cold Start Time Is The First Problem

AI container images often exceed 20GB due to model weights and dependencies like CUDA and PyTorch. The standard startup process involves downloading all image layers, decompressing them and mounting the file system. For a 20GB image, this can take over 10 minutes. When your autoscaler spins up a new pod and it takes 10 minutes to serve traffic, you are either over-provisioned and wasting money or under-provisioned and dropping requests. At Tensorfuse, we built a lazy-loading snapshotter that reduces this to seconds by fetching only the files needed at runtime.

Standard Load Balancing Is The Second Problem

Basic K8s routing does not account for AI-specific patterns. You need queue-aware routing to avoid sending requests to overloaded instances, cache-aware routing to reuse KV cache where possible and latency-aware distribution to meet service level agreements (SLA). For critical workloads, run multi-region at a minimum.

4. Observability

You cannot optimize what you cannot measure. For AI inference, track three categories of metrics:

1. Resource metrics tell you how efficiently you are using your hardware. GPU utilization is the percentage of time your GPUs are actively processing workloads. GPU memory usage tracks framebuffer consumption: total, used and free. If utilization is low while you pay for full capacity, you are wasting money. Tools like Nvidia DCGM expose these metrics in Prometheus format.

2. Request metrics tell you what users experience. It’s important to track latency percentiles: p50 is your median response time, p95 captures the experience of your slower requests and p99 shows your worst-case latency. For LLMs, measure time-to-first-token (TTFT) separately from total generation time since users perceive these differently. Also, track tokens per second, error rates and queue depth.

3. Cost metrics connect infrastructure to business outcomes. Calculate cost per inference request and cost per million tokens. Use your cloud provider’s billing API or tools like Kubecost to get hourly GPU costs, then divide by request volume. This number tells you whether your inference operation is sustainable.

The Bottom Line

The Nvidia-Groq deal is a $20 billion signal about where AI infrastructure is heading. Inference is becoming the bottleneck. Training got us here. Inference determines who can scale.

If you are planning on running AI in production, make sure you consider the four challenges presented here first.

Nvidia just bet $20 billion that inference is the future. Is your infrastructure ready?

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