Continuous batching torched our inference bill. We ran naive batching before—one request in, one out. Throughput? Fifty tokens per second. Embarrassing for production. Switched to vLLM's continuous batching: 450 tokens. Same GPUs. 9x jump. Anyscale benchmarks put it at 23x versus naive. Our GPU spend dropped 40%. Finance went quiet. 4-bit quantization added 2.69x throughput, 98.1% accuracy intact. Useful. But batching was the sledgehammer. PagedAttention gutted KV cache waste—60-80% down to under 4%. Stack them and you're looking at 10-25x efficiency gains without buying new boxes. The receipt leadership needed: $180K monthly GPU bill became $108K. Same queries. Same quality.
AWQ Standardizing the 4-bit Quantization was a game changer for our inference costs. Most engineering teams are concerned with quality drop-off with quantization, but the trade-off for almost all enterprise level workflows is so small that the throughput gains are incredible. We were able to increase our tokens-per-dollar by a factor of three by moving workloads from multiple A100 instances to far less expensive L4 instances. A 62% decrease in monthly computing expenses at the same request volume is what convinced our finance group to begin investing in L4 instances. By proving that you're not only saving nickels but also completely changing the hardware tier necessary to support your product, you transition the focus from addressing technical debts to operating efficiently. Internal benchmarking aligned with industry standard research that states that 4-bit quantization can achieve a speedup factor of 3x in throughput when comparing to FP16 on modern GPU architectures. Getting lost in the hype of large models is typical, but the true engineering challenge is to maximize the efficiency and leanest operation of the models you have. Focusing on memory efficient quantization is the fastest path from high-cost experimental AI to a viable production offering.
A tactic that meaningfully improved tokens per dollar for LLM inference is request batching: instead of sending each user request to the GPU one by one, you briefly "collect" several that arrive close together and run them in one go. Why it helped: A single small request can leave the GPU partly idle. Batching keeps the GPU busy, so you get more text created per hour of GPU time. You're just using the same hardware more efficiently, the quality stays the same. The before/after that actually worked for finance/product was more throughput at the same latency SLO ("we can serve X requests/sec while keeping p95 under Y ms") not "the model is faster in a benchmark." A real world example I've used is that the NVIDIA Triton docs say that turning on dynamic batching hit 272 inferences/sec with 8 concurrent requests without adding latency in their example setup. It's easy to see how this translates to dollars. if the same GPU can handle more steady state traffic without changing the user experience, your cost per request and therefore cost per token goes down, which makes it easy to standardize.