LLM Optimization
LLM optimization for faster, cheaper production inference
Inwire.ai optimizes LLM inference with engine selection, GPU fit analysis, quantization, batching, KV cache tuning, routing, autoscaling, benchmarking, and cost observability.
Fast answer
Inwire.ai improves LLM throughput and latency by tuning inference engines, GPU fit, quantization, batching, KV cache, speculative decoding, routing, autoscaling, and cost-per-token observability across vLLM, SGLang, TRTLLM, Triton, and TGI deployments.
Production outcomes
Reduce latency and cost per inference with benchmark-backed runtime choices.
Tune quantization, batching, KV cache, GPU profiles, and autoscaling settings.
Route traffic across model variants and deployment targets based on performance, cost, and reliability.
Benchmark before changing production settings
Inwire.ai compares runtime engines, GPU families, quantization levels, and serving topologies against real workload requirements before recommending an LLM optimization plan.
Optimization across the full inference stack
Improve LLM serving through vLLM, SGLang, TGI, TensorRT-LLM, TRTLLM, Triton, ONNX Runtime, batching, KV cache tuning, speculative decoding, and GPU memory planning.
Cost and reliability are optimization targets
Optimization covers more than speed. Inwire.ai tracks throughput, latency, GPU utilization, error rates, fallback behavior, and cost per request.
What inwire.ai can run and optimize
Benchmark vLLM, SGLang, TensorRT-LLM, TRTLLM, Triton, TGI, ONNX Runtime, and llama.cpp for each model and workload.
Tune throughput with batching, continuous batching, KV cache sizing, prefix caching, tensor parallelism, and request routing.
Reduce latency and cost with FP16, FP8, INT8, INT4, AWQ, GPTQ, speculative decoding, pruning, and model compression.
Track tokens per second, time to first token, GPU utilization, memory pressure, queue depth, error rate, and cost per inference.
Questions teams ask before rollout
What is LLM optimization?
LLM optimization improves how a language model runs in production by tuning serving engines, GPU usage, quantization, batching, KV cache, routing, autoscaling, and monitoring for higher throughput and lower latency.
Can Inwire.ai optimize existing LLM deployments?
Yes. Inwire.ai can benchmark existing deployments, identify bottlenecks, recommend runtime and GPU changes, and produce production-ready optimization settings.