Tokens-Per-Dollar: Your Best LLM Inference Win
Getting the most value from large language model inference doesn't require complex optimization strategies. Industry experts reveal that implementing continuous batching can dramatically reduce costs while maintaining performance. This straightforward technique delivers one of the biggest efficiency gains available to teams running LLM workloads.
Use Continuous Batches to Slash Spend
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.
Pair Draft and Judge Models for Speed
Speculative decoding lets a small draft model guess ahead while a stronger model checks the guesses. When the guesses are right, the strong model accepts many tokens at once, which lifts throughput. The gains rise with longer answers and higher accept rates.
Draft size, sampling settings, and batch shape can be tuned to hit a sweet spot. Co-locating both models and reducing hops keeps latency low. Try a draft model two sizes smaller than your main model and tune for accept rate today.
Reuse Prefix Caches to Skip Recompute
Many requests repeat the same long prefix, such as a policy block or a knowledge header, so caching saves work. By reusing the KV cache for that prefix, the model skips recomputing attention over tokens it has already seen. This gives speed and cost wins on sliding windows and multi-turn chat.
Share cache entries across users only when the prefix is truly the same to avoid leaks. Invalidate caches when the shared text changes to keep outputs correct. Add prefix caching to your serving stack and measure the drop in cost per turn now.
Quantize to 4-Bit and Preserve Accuracy
Quantizing a model to 4-bit cuts memory use and speeds up each step without a big hit to quality. Modern methods keep the hard parts of the network in higher precision where needed, so accuracy stays close. With smaller weights, a single GPU can serve larger batches, which lifts tokens per dollar.
Calibrate on a small, clean set to avoid drift on rare words and numbers. Keep the KV cache in higher precision if latency or stability dips. Run a 4-bit trial on your main task and measure cost per token to pick the best plan today.
Shorten Prompts and Drop Boilerplate
Shorter prompts pay off right away because every token sent has a direct cost. Trim system messages to only the rules that shape tone and safety, and drop repeated boilerplate. Replace long few-shot examples with briefer patterns or a compact rubric.
Move static text out of the prompt and refer to it by a short tag or version number. Track output changes so that safety and quality stay steady as words are removed. Cut your prompts this week and watch your token bill fall.
Distill With Retrieval to Reduce Compute
Distilling a large model into a smaller one drops compute costs while keeping the skills that matter. A teacher model can label task data, and the student learns the steps, style, and guardrails. When paired with retrieval, the small model pulls facts from a store, so it does not need to keep every detail in its weights.
This allows short prompts and steady quality on niche topics. The mix often beats a bigger model that lacks search, especially under tight budgets. Set up a simple retriever and train a small student on key tasks, then compare quality and spend now.