Squeezing the Inference Lever: The Economics of LLM Throughput

Most people treat the cost of running a Large Language Model as a bill they have to pay. They look at the price list from a cloud provider and assume that’s the market clearing price.

But if you’re building at scale, the price of a million tokens is actually a function of your engineering team’s ability to manage memory and compute. It is an optimization problem, and it has three distinct levers.

To lower costs without losing quality, you have to look at Model Compression, Runtime Optimization, and Deployment Architecture.

1. Model-Level Compression

The first lever is the model itself. If you can make the model smaller without making it stupider, you win.

• Quantization: This is the most common move. By reducing numerical precision-moving from FP16 to INT8 or even INT4-you reduce the VRAM footprint. This isn’t just about fitting more on a chip; it’s about reducing the total memory bandwidth required to generate each token.
• Knowledge Distillation: You don’t always need the “Teacher” model to do the work. A smaller “Student” model, trained to mimic the massive model, can often handle specific tasks at 1/5th the cost.
• Pruning: Most models are heavier than they need to be. Pruning removes redundant attention heads or weights that contribute little to the output. It’s the architectural equivalent of trimming the fat.

2. Runtime & Serving Optimizations

Once you have the model, the second lever is how you serve it. The goal here is to maximize the utilization of your hardware.

• Continuous Batching: Static batching is inefficient because users generate tokens at different speeds. Continuous batching dynamically injects new requests as soon as old ones finish. This can increase throughput by an order of magnitude.
• KV Caching & PagedAttention: Every time a model generates a token, it usually re-calculates the entire context. KV caching stores those intermediate values. Frameworks like vLLM use PagedAttention to manage this memory in blocks, preventing fragmentation and allowing higher concurrency.
• Speculative Decoding: This is a clever trick. You use a tiny, fast model to predict the next few tokens, and then use the big model to verify all of them in one pass. It’s like having a fast typist and a careful editor working in parallel.
• FlashAttention: This is a hardware-level optimization. By minimizing the reads and writes between the HBM and the GPU cache, it solves the “Memory Wall” problem, especially for long-context prompts.

3. Architectural & Deployment Strategies

The third lever is the system architecture. This is where you move from “running a model” to “building a platform.”

• Model Routing: You shouldn’t use a PhD-level model to check for spam. A router analyzes the query and sends simple tasks to small models, reserving the expensive “frontier” models only for complex reasoning.
• Semantic Caching: If two users ask the same question, you shouldn’t ask the LLM twice. By using vector databases to cache semantically similar responses, you can bypass the LLM entirely for up to 90% of repeat traffic.
• Parallelism: For truly massive models, you have to split the work. Tensor Parallelism splits the layers themselves across devices, while Pipeline Parallelism stages the execution to keep every GPU busy.
• Prompt Caching: If your system prompt or few-shot examples are 5,000 tokens long, you shouldn’t re-compute them for every request. Prompt caching ensures you only pay for the computation of the static prefix once.

The Bottom Line

In the early days of a technology shift, people overpay for everything because they just want it to work. We are now moving into the phase where efficiency is the primary moat.

The companies that win won’t just be the ones with the best models. They’ll be the ones who realized that “Inference” is a variable cost that can be compressed through engineering discipline. Stop looking at the price list. Start looking at the levers.