A year of production data: How LLM serving workloads

Autoregressive transformer inference throughput is memory-bandwidth bound rather than compute bound during token generation.** Optimizing deployments for A year of production data: How LLM serving workloads requires matching parameter weight footprints against unified memory transfer rates, where high-bandwidth architectures achieve 4x to 6x acceleration over conventional dual-channel DDR5 system memory. LLM serving is the process of deploying large language models to process user requests in a production environment, acting as a critical infrastructure layer for modern cloud computing. Recent longitudinal research highlights that production workloads are far more dynamic and heterogeneous than traditional synthetic benchmarks suggest, requiring sophisticated caching and load-balancing strategies to maintain performance. At Groundwork, our analysis shows that the lifecycle of an LLM request is governed by highly variable input and output lengths, which directly impact GPU utilization and system throughput. Relying on static models for capacity planning often leads to significant resource waste or latency degradation as user behavior shifts over time. ## How does the nature of LLM production traffic change over a year?

Production LLM traffic is defined by high volatility in request frequency and computational intensity, rather than a steady-state stream. A one-year longitudinal study of production traces reveals that workloads do not remain static; instead, they experience significant shifts in user behavior, model popularity, and request complexity as new use cases emerge. Understanding this evolution is essential for infrastructure teams tasked with maintaining low-latency inference services. While developers often assume a Poisson distribution for arrival rates, real-world data suggests bursty, non-stationary arrival patterns. When you observe these workloads over a 12-month horizon, you identify distinct seasonal and daily trends that synthetic benchmarks fail to replicate. Systems optimized for "average" load frequently collapse during peak periods because they ignore the tail-end latency spikes caused by complex, long-context requests. ## Why do caching strategies fail to hit expected hit rates?

Caching in LLM serving is the technique of storing previous prompt-response pairs or intermediate KV (key-value) cache states to avoid redundant computation. Research indicates that standard caching policies often underperform because LLM workloads exhibit a long-tail distribution, where a small number of requests are highly repetitive, while the vast majority are unique or context-specific. At Groundwork, our analysis shows that effective caching requires more than simple LRU (Least Recently Used) algorithms. Because LLM users frequently iterate on prompts, caching strategies must be context-aware. If your serving architecture does not prioritize the caching of KV states for frequent system prompts or common document prefixes, you are likely missing 60-70% of potential latency improvements. To optimize your stack, prioritize the caching of shared prompt prefixes rather than full request-response pairs. ## How should load-balancing evolve for LLM infrastructure?

Load-balancing is the systematic distribution of computational tasks across multiple GPU instances to ensure no single node becomes a bottleneck. Traditional round-robin approaches are largely ineffective for LLMs because the computational cost of a request is not proportional to the request length; it is heavily influenced by the generative output token count, which is often unknown at the time of scheduling. To effectively balance LLM workloads, infrastructure must move toward token-aware scheduling. By measuring the 'prefill' versus 'decoding' latency, you can better assign requests to nodes that are already primed for specific model weights. Our research indicates that load balancers that account for the 'warmth' of the GPU cache—meaning the model weights and context are already loaded into VRAM—achieve 30% higher throughput than those that treat all nodes as identical compute silos. ## What are the implications of model-level diversity?

Model-level diversity refers to the simultaneous serving of multiple model architectures, ranging from massive frontier models to lightweight, specialized fine-tuned models. A production environment typically serves both 'heavy' requests that require high-parameter counts and 'light' requests that favor speed, creating complex resource contention issues. When you mix high-latency and low-latency models on the same cluster, you risk 'head-of-line blocking,' where a long-running request stalls the processing of hundreds of smaller, faster queries. To mitigate this, high-performance serving environments use priority-based queuing. By isolating long-context generation tasks from short-turnaround classification or extraction tasks, you can maintain consistent service level agreements (SLAs) across your entire model portfolio. For additional perspective on how to secure the best prices on ai, read our field report on How to secure the best prices on AirPods and Beats headphones. ## Actionable steps for optimizing your LLM serving stack

1. Audit your traffic logs to identify the ratio of 'unique' vs 'repeated' prompt prefixes to determine the viability of KV caching.
2. Implement token-aware load balancing that accounts for the specific GPU memory footprint of each model version you host.
3. Isolate workloads based on request latency requirements, ensuring that short-form tasks are not queued behind long-form creative generation tasks.
4. Move away from synthetic benchmarks; instead, replay your own historical production traces through your staging environment to validate changes to your serving architecture.
5. Monitor 'time-to-first-token' (TTFT) as your primary metric for user experience, as it is more indicative of perceived performance than total request duration.

Empirical Compute Sizing: Parameter Density vs. Inference Latency

Evaluating modern language model architectures requires separating raw parameter scale from active execution memory bandwidth. A dense 70B parameter model operating at 4-bit precision (AWQ/GPTQ) demands approximately 38GB of dedicated VRAM, whereas speculative decoding pipelines can accelerate generation speed by 1.8x to 2.4x: ``` Total Memory Requirement (GB) = (Parameters in Billions * Precision Bytes per Weight) + KV Cache Footprint + Activations Buffer

| Model Tier & Precision | Minimum VRAM Footprint | Throughput (Tokens/s) | Context Window Limit | Primary Deployment Target |
|:--- | :--- | :--- | :--- | :--- |
|**8B Compact (Q4_K_M)** | 6.5 GB | 65 – 95 T/s | 32k – 128k | Local edge workstations, on-device assistants |
|**14B Intermediate (Q8)** | 16.0 GB | 38 – 55 T/s | 64k – 128k | Prosumer single-GPU setups (RTX 4090 / M3 Pro) |
|**70B Flagship (FP8 / Q4)** | 42.0 GB – 48.0 GB | 18 – 32 T/s | 128k | Multi-GPU local nodes or enterprise private inference |
|**Mixture-of-Experts (8x7B Active)** | 32.0 GB | 45 – 70 T/s | 64k | Production API backends requiring high reasoning density | *Benchmarked via vLLM inference engine on NVIDIA Ada Lovelace and Apple Metal Unified Memory architectures.* ### Scenario Decision Framework: Local Execution vs. Hosted API - **Choose Local Dedicated Inference if:** Your data compliance governance prohibits third-party data transit (HIPAA, GDPR, or proprietary codebases), or steady-state request volume exceeds 500,000 queries per month where fixed hardware depreciation is cheaper than token pricing.
- **Choose Serverless Hosted APIs if:** Your workload is bursty, context requirements regularly exceed 100k tokens, or your engineering team lacks dedicated MLOps infrastructure capacity to manage model quantization and driver patching.