AI systems can appear magical until you’re sitting around waiting for them to answer. Whether it’s a large language model (LLM) in the cloud, an on‑device vision model or a recommendation system, inference involves huge neural networks, massive numbers of parameters and a lot of data movement. In practice this means expensive GPUs, high network latency and occasional stalls when a remote service is overloaded. The good news is that you don’t have to simply accept the lag. A wave of research and tooling over the last year shows that careful engineering can make AI feel responsive while saving money. This guide distils the latest optimization techniques into practical speed tricks anyone can use to stop waiting on AI. Understand Where the Time Goes Understand Where the Time Goes Prefill vs. Decode Phases Prefill vs. Decode Phases LLMs generate text in two phases. In the prefill phase, the model processes the entire input prompt and computes intermediate key and value tensors that capture the context. These operations are highly parallelizable and saturate GPUs. In the decode phase, the model prefill phase decode phase produces output tokens one at a time, reusing cached keys and values from the prefill. This sequential process is memory‑bound and often under‑utilizes the GPU. Latency mainly comes from the decode phase, so optimizations that reduce memory traffic (like caching, quantization or batching) directly translate into faster responses. Synchronous vs. Asynchronous Logic Synchronous vs. Asynchronous Logic A different reason for delay is the way your application makes calls to external services. A synchronous model means you'll need to wait for every network call to finish before your application can move on to something else. This is easier to link together in code, but causes your program to run slower because it will run through a series of tasks, waiting for each task to finish before going to the next step. Asynchronous programming will run through multiple tasks at the same time, and has a non-blocking model so your program can run other tasks while waiting for I/O, increasing responsiveness and throughput. This could be useful in any AI workloads where the workload is fetching data, calls an API or loads a model - async frameworks will have reduced some of the idle time and will keep your UI responsive. Use Smarter Models Use Smarter Models Compress Models Without Losing Accuracy Compress Models Without Losing Accuracy Large models are powerful but slow. Reducing their size is the simplest way to speed up inference. Model compression techniques shrink neural networks without significantly affecting accuracy. The key methods are: Model compression ●      Quantization — converting weights from 32‑bit floats to lower‑precision formats like INT8 or FP16 reduces memory and accelerates computation. Quantizing LLMs can halve memory usage and speed up inference. Quantization ●      Pruning — removing redundant neurons and connections reduces the number of operations without sacrificing quality. Pruning ●      Knowledge distillation — training a smaller “student” model to mimic a large “teacher” model delivers comparable performance with far fewer parameters. Knowledge distillation Optimizations at the graph level also matter. Frameworks like ONNX Runtime and TensorRT can fuse operations, eliminate redundancies and optimize memory access. These graph optimizations provide significant performance improvements. Combined with quantization, they often double throughput without harming accuracy. graph optimizations Choose Efficient Architectures Choose Efficient Architectures Modern transformer research has produced more efficient attention mechanisms. Flash Attention computes attention scores with reduced memory bandwidth, while sparse attention limits calculations to a subset of tokens. Flash Attention sparse attention Multi‑query or grouped‑query attention similarly reduce the size of key‑value caches. When selecting or fine‑tuning a model, look for these improvements—newer architectures can produce similar quality at a fraction of the compute cost. Use Smaller Contexts and Adaptive Computation Use Smaller Contexts and Adaptive Computation LLMs allow long context windows, but sending huge prompts every time wastes bandwidth. Summarize previous messages or documents and provide only the relevant context. Some models include early‑exit mechanisms that stop processing once a confident prediction is reached. Conditional computation activates only parts of the network for simple inputs. These features reduce computation and shorten early‑exit mechanisms response times, making AI feel more interactive. Optimize Attention And Caching Optimize Attention And Caching Exploit Key‑Value (KV) Caching Exploit Key‑Value (KV) Caching Caching intermediate results is one of the most effective ways to speed up LLMs. During the prefill phase, the model stores key and value tensors (the KV cache) for all input tokens. Reusing this cache in the decode phase avoids recomputing attention for every step. KV cache Persistent KV caches are particularly valuable in multi‑turn chats or retrieval‑augmented generation (RAG) pipelines. Maintaining a KV cache prevents re‑processing the same input tokens and saves the cost of reloading large contexts. Loading a pre‑computed KV cache can reduce the “time to first token” dramatically, making responses almost instantaneous. However, caches consume GPU memory. Frameworks like vLLM and LMCache manage KV caches efficiently and offload them to CPU or remote storage when idle. NVIDIA’s Dynamo infrastructure even transfers KV caches across GPUs and storage using high‑speed RDMA. By persisting and sharing caches you can serve longer contexts and higher throughput without buying more GPUs. Use Efficient Batching Use Efficient Batching Running multiple requests together spreads the cost of model weights across them and improves GPU utilization. Static batching groups requests with similar lengths but can be inefficient when their response times vary. Dynamic or in‑flight batching assembles batches on the fly based on arrival times and adapts to varying input lengths. Static batching Dynamic or in‑flight batching Many inference engines implement continuous batching to keep GPUs busy and achieve significant throughput gains. Cloudflare’s Infire engine, for example, employs a sophisticated batcher to process hundreds of concurrent connections while maximizing memory and network I/O efficiency; benchmarking shows that Infire completes inference tasks faster than comparable implementations. Parallelize Across Devices Parallelize Across Devices Large models sometimes exceed a single device’s memory. Model parallelism splits model layers across multiple GPUs, while pipeline Model parallelism pipeline parallelism runs different parts of the model on different devices in sequence. parallelism Tensor parallelism distributes the computation of large matrices across GPUs to improve efficiency. Use frameworks like DeepSpeed, Megatron-LM or TensorRT‑LLM to manage these configurations. Keep in mind that parallelism introduces communication overhead, so profile carefully to find the sweet spot. Tensor parallelism Embrace Mixed Precision And Hardware Tricks Embrace Mixed Precision And Hardware Tricks Modern GPUs contain specialized Tensor Cores designed for lower‑precision arithmetic. Running inference with FP16 or BF16 precision can double throughput compared with FP32 while preserving quality. Tensor Cores Automatic mixed‑precision tools dynamically adjust precision at different layers. When deploying on GPUs that support INT8 or even FP4 operations, quantize models accordingly to unlock additional speed and energy savings. Beyond precision, pay attention to memory layout. Contiguous memory layouts and cache‑friendly tensor shapes improve bandwidth and reduce latency. Loading model weights efficiently also matters. memory layout Cloudflare’s Infire engine uses page‑locked memory and CUDA asynchronous copy to transfer model weights into GPU memory over multiple streams; this parallelizes model loading and just‑in‑time kernel compilation, enabling the 8B Llama‑3 model to start up in under four seconds. Such low‑level optimizations require expertise but illustrate the gains possible when every byte of bandwidth is considered. Adopt Asynchronous Programming and Concurrency Adopt Asynchronous Programming and Concurrency Run Tasks in Parallel Run Tasks in Parallel When your application needs to call an external model API, fetch data, and process results, avoid serializing these operations. Asynchronous programming lets you kick off long‑running tasks and then do other work while waiting. Asynchronous programming is a non‑blocking architecture that sends multiple requests to a server simultaneously, increasing throughput and improving responsiveness. Choose async for independent, parallelizable tasks to keep your app responsive. In practice this could mean fetching multiple documents concurrently, sending batched requests to an LLM API, or using event loops (e.g., JavaScript’s async/await or Python’s asyncio) to handle I/O without blocking CPU threads. When combined with connection pooling, caching and dynamic batching, asynchronous programming eliminates most of the waiting perceived by users. Offload Work With Message Queues Offload Work With Message Queues Asynchronous design also means decoupling tasks. Instead of having a single process fetch emails, call an LLM, summarise the result and send notifications, break the workflow into separate services and connect them with a message queue. This is the approach used in the Symfony Messenger example in our reference article. Decoupling ensures that if one service (for example, the model server) is temporarily unavailable, other parts of the system can continue to work. When the service comes back online, queued messages are processed without blocking the main application. Cache and Reuse Responses Cache and Reuse Responses Finally, avoid repeated computation whenever possible. Caching is more than just storing key‑value tensors. If your application performs expensive operations (like summarizing a document or generating an embedding), save the result with a hash of the input. Next time the same input is requested, return the cached output. Pair this with mechanisms to invalidate or refresh stale entries. On the client side, browsers or mobile apps can implement simple caches to avoid sending duplicate requests to the server. When working with generative models that include randomness, set a deterministic random seed for cached responses to remain consistent. Conclusion Conclusion The perception that AI is inherently slow is a myth. With thoughtful engineering, even massive models can respond in near real time. The first step is understanding the phases of inference and where latency originates. From there, you can cut wait times by compressing models, using efficient attention mechanisms, caching intermediate states, batching requests, parallelizing across devices and exploiting mixed precision. Cloudflare’s bespoke Infire engine demonstrates that implementing these techniques can make inference faster than state‑of‑the‑art servers. For everyday developers, adopting asynchronous programming and message queues keeps applications responsive and hides the inherent slowness of external services. Inference optimization isn’t just about speed—it also delivers better price‑performance and substantial infrastructure savings. By applying the speed tricks outlined here, you can stop waiting on AI and start building experiences that feel instantaneous.

This story contains new, firsthand information uncovered by the writer.

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