LLMs are getting slower. Here’s why that matters

Large language models aren't just getting smarter. They're also getting slower. GPT-4-Turbo, Claude 3, Gemini all show increased latency compared to their earlier versions. For most users that’s an inconvenience. For developers building real products, it’s a bottleneck.

There are a few reasons this is happening:

1. Bigger models, slower inference Despite the “Turbo” branding, newer models are larger and more complex under the hood. That means more tokens to process, longer context windows to search through, and more compute needed per response. Inference time is increasing, especially for multi-turn conversations and large payloads.

2. Caching is doing heavy lifting What feels fast in demos is often cached output. But in production, where user prompts are varied and dynamic, cache hits are rare. If you’re building chat products or API-based tools, you’re likely seeing the full latency, and it’s growing.

3. Cost trade-offs at the infra level OpenAI and Anthropic are managing huge inference costs. Slower speeds help them reduce GPU demand, batch more requests, and throttle load. Some developers suspect deliberate pacing to cut infra spend or prioritise enterprise users.

4. The illusion of choice There’s a growing list of models, but most run on the same backends: Azure, GCP, AWS. They also rely on similar transformer architectures. You can’t avoid the slowdown by switching models. The issue is structural.

Why it matters Latency isn’t just UX. It affects what you can build. A 500ms delay in autocomplete makes a coding assistant feel broken. A 4-second wait kills flow in an AI note-taker. As models slow down, devs are quietly reducing the number of LLM calls, offloading tasks to local models, or ditching real-time AI features altogether.

Until inference speed improves, or edge deployment becomes viable, many “AI-first” product ideas are on pause. There’s only so much UX you can fix with spinners.

How teams are working around it

Developers aren’t sitting still. Here’s what’s gaining traction:

- Streaming output: Even if full responses are slower, streaming gives the illusion of speed and keeps users engaged. It’s not perfect, but it’s better than blank loading states.

• Trimming context: Many apps are optimising prompt length with summarisation, context pruning, or smarter chunking. Less in, faster out.
• Early exits and low-temp runs: For deterministic tasks, some apps cut output short or use aggressive temperature settings to speed up generation.
• Hybrid models: Open-source models like Mistral or Phi are being used for simpler tasks, leaving the heavy lifting to GPT-4 or Claude only when needed.
• Background processing: In async workflows, LLMs are being pushed to the backend. That means results appear later, but don’t block the interface.
• User patience tricks: Skeleton loaders, staged output, and quick placeholder text are being used to buy time, not ideal, but better than freeze-ups.

  For now, speed is being engineered around rather than solved. But the workaround mindset is shaping what gets built — and what doesn’t.