The Part of AI That Users Actually Touch
LLM inference is the moment an AI system stops learning and starts performing. Every token your users see — every chatbot reply, voice assistant response, or AI-powered recommendation — is the product of inference. Training gets the headlines, but inference is where the money goes: as early IBM research estimated, up to 90% of an AI model's lifetime compute is consumed at inference time, not during training.
That ratio has real consequences for anyone building AI products. Understanding how inference works — and where it breaks down — is no longer optional for engineering teams that want to ship fast, reliable systems. The Turing Post's deep dive on inference captures the field's current momentum well, but we want to frame it around the decisions that matter when you're deploying to production.
What "Inference" Actually Means at Runtime
Inference is the end-to-end process that begins when a user submits a prompt and ends when the model returns a complete response. Inside a transformer-based LLM, that process runs through several tightly coupled stages.
First, raw text is converted into token IDs — numeric representations drawn from the model's vocabulary. Those IDs are mapped to dense embedding vectors that carry semantic meaning, with positional information layered in so the model understands word order. If you want a deeper look at how that meaning is encoded geometrically, our post on how tokens gain meaning through AI embeddings covers the mechanics in detail.
From there, the token sequence passes through stacked transformer layers — self-attention and feedforward networks working in concert to extract context. The model outputs logits: raw, unnormalized scores across its entire vocabulary for what the next token should be. A decoding strategy (greedy, top-k, beam search, or sampling) selects the winner. That token gets appended to the input, and the whole loop repeats until a stop condition is met.
Each repetition is called an autoregressive step, and it's why inference latency compounds with output length.
Latency vs. Throughput: The Core Trade-Off
Two metrics define inference performance from an operator's perspective, and they pull in opposite directions.
Latency measures how long it takes to return one result. It breaks down into Time to First Token (TTFT) — the delay before the model starts responding — and Time Per Output Token (TPOT) — the pace at which subsequent tokens arrive. In streaming applications, users feel both. In non-streaming settings, they feel only the total wall-clock time.
Throughput measures how many inferences or tokens the system completes per second across all concurrent users. Batching requests together improves throughput dramatically, but it increases individual latency because each request waits for others to fill the batch.
Every production AI system lives somewhere on that curve. The right operating point depends entirely on the use case: a voice assistant needs aggressive latency minimization; an overnight document-processing pipeline can trade latency for throughput gains. Working on a deployment where that balance isn't obvious? Talk to our team about your project.
Why Optimization Is Non-Negotiable
The gap between a naive inference deployment and an optimized one is not small. These are the techniques we reach for on every LLM build:
KV Caching stores the key and value tensors computed during the attention pass for every token already processed. Without it, the model recomputes all previous context on every autoregressive step — an O(n²) problem that makes long outputs painfully slow.
Quantization reduces weight precision from 32-bit floats down to 8-bit integers or even 4-bit representations. NVIDIA's Blackwell architecture combined 4-bit precision with kernel-level software optimizations to achieve 36× higher inference throughput and roughly 32× lower cost per token compared to previous baselines — without retraining the model.
Speculative decoding uses a smaller draft model to generate candidate tokens in parallel, then validates them against the full model in a single forward pass. When the draft is right — which it often is for predictable spans of text — latency drops by 30–50%.
Batching and continuous batching pack multiple user requests into one matrix operation. Static batching fixes the batch size upfront; continuous batching, as implemented in engines like vLLM, dynamically slots new requests into active compute cycles, pushing GPU utilization closer to theoretical maximums.
Model distillation and pruning shrink the model itself — distillation trains a smaller student model to replicate a larger teacher's behavior; pruning removes weights that contribute little to output quality. Both reduce the raw compute required per token.
For teams building on top of open models, the combination of KV caching, quantization, and continuous batching is usually where we start. Our work on RAG and LLM development puts these techniques into practice across retrieval-augmented pipelines where context windows are long and latency budgets are tight.
The Inference Scaling Shift
Something more fundamental than optimization tricks is changing the inference landscape. Reasoning models — think OpenAI o1, DeepSeek-R1 — deliberately spend more compute at inference time to improve answer quality. NVIDIA CEO Jensen Huang has named this "test-time scaling" as a third major scaling law alongside pre-training and post-training.
Faster systems don't just respond quicker — they create space to reason more deliberately at runtime.
This creates a compounding dynamic: as hardware gets faster, models can afford more reasoning steps within the same latency budget. Cerebras demonstrated this concretely when software-only optimizations to its wafer-scale engine reached 2,100 tokens per second on a 70B-parameter model — 16× faster than comparable GPUs — which opened the door to models evaluating hundreds of reasoning paths in real time rather than committing to the first plausible answer.
NVIDIA's Dynamo framework pushes this further by separating the prefill phase (embedding the prompt through the model) from the decode phase (generating output tokens) and routing them to different GPUs concurrently. Dynamic KV cache reuse and intelligent load balancing across nodes push throughput up by as much as 30× compared to monolithic inference servers.
The architectural insight behind both systems is the same: latency and throughput are not fixed properties of a model — they are engineering choices constrained by hardware, software, and system design. Teams that understand the full stack can move those constraints significantly.
Understanding this shift also changes how we think about agent architectures. When inference is fast and cheap enough, multi-step AI agents that call tools, verify outputs, and retry on failure become practical for real-time user-facing applications — not just offline pipelines.
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LLM inference is the operational reality behind every AI product, and the gap between naive and optimized deployments spans orders of magnitude in cost, speed, and user experience. The most important shift happening right now is that faster inference doesn't just mean cheaper responses — it means models can reason more carefully within the same latency window, fundamentally raising the quality ceiling. Teams that treat inference as an engineering discipline rather than a black box will build products that are faster, cheaper, and more capable all at once.
“Faster systems don't just respond quicker — they create space to reason more deliberately at runtime.”
