AI Inference Optimization

Q: Next Steps

If you're deploying models to production, see [AI Model Deployment 2026](/en/rehberler/ai-model-deployment-2026) for the full ops picture around serving, monitoring, and rollback. - If you're a backend engineer integrating inference into a larger system, [AI for Backend Developers 2026](/en/rehberler/ai-backend-developers-2026) covers the API integration and reliability patterns. - For SREs managing inference infrastructure at scale, [AI for SRE 2026](/en/rehberler/ai-sre-2026) covers observability, alerting, and capacity planning for model-serving systems.

AI Inference Optimization 2026 TL;DR. Most teams overpay for inference by 3-10x because they skip three things: quantization, KV cache configuration, and batching strategy. Fix those first. Hardware and serving framework choices matter but are secondary to getting the model configuration right. Why Inference Is Not Just "Running the Model" Training a model is a one-time cost. Inference runs continuously against real users, real latency budgets, and real billing cycles. A model that performs well on a benchmark can still destroy your unit economics in production if you're materializing full-precision weights, processing requests serially, or allocating KV cache naively. The gap between a badly-served model and a well-served one is not 10-20% — it can be a 5-10x difference in throughput and cost per token. AI inference optimization is where that gap closes. The core problem: transformer inference is memory-bandwidth-bound, not compute-bound, for most deployment configurations. You're waiting on VRAM reads, not GPU arithmetic. Every technique in this guide attacks that bottleneck from a different angle. Quantization: INT8, INT4, and When to Use Each Quantization reduces weight precision, which cuts memory bandwidth and VRAM requirements. INT8 (W8A8 or W8A16) - 2x memory reduction vs FP16 - Negligible quality loss on most tasks (perplexity degradation < 1%) - Safe default for production deployments - Supported natively by vLLM, TGI, TensorRT-LLM INT4 (GPTQ, AWQ, GGUF Q4 K M) - 4x memory reduction vs FP16 - Quality loss is model-dependent — acceptable for most chat tasks, noticeable on complex reasoning - Enables running 70B-class models on consumer hardware (2x 48GB GPUs instead of 4) - llama.cpp Q4 K M is the practical default for local/edge deployments The tradeoff matrix: | Format | VRAM | Throughput | Quality | Best for | |--------|------|-----------|---------|----------| | FP16 | 100% | baseline | reference | fine-tuning, evals | | INT8 | 50% | 1.3-1.8x | ≈FP16 | production API | | INT4 | 25% | 1.5-2.5x | slight drop | cost-sensitive, edge | | INT4 (Q4 K M) | 25% | CPU-viable | slight drop | local inference | Start with INT8. Move to INT4 only after measuring quality impact on your specific task distribution. KV Cache: The Most Undertuned Knob The KV (key-value) cache stores attention computation results across tokens so the model doesn't recompute them on every forward pass. It's what makes autoregressive generation tractable. The problem: KV cache allocation is often left at defaults, which either wastes VRAM or causes costly evictions under load. What to tune: - Cache block size: vLLM uses paged attention with configurable block sizes. Larger blocks reduce fragmentation for long contexts; smaller blocks are more efficient for short requests. - GPU memory utilization: vLLM's --gpu-memory-utilization defaults to 0.90. For mixed-length workloads, 0.85 gives headroom; for long-context workloads, you may need to tune this alongside --max-model-len . - Prefix caching: vLLM supports automatic prefix caching (APC). If your system prompt is long and consistent across requests — like a RAG context, a coding assistant system prompt, or a customer service persona — enable prefix caching. First-token latency drops dramatically for cached prefixes, often 40-60% on real workloads. bash vllm serve mistral-7b-instruct \ --gpu-memory-utilization 0.87 \ --enable-prefix-caching \ --max-model-len 16384 Prefix caching is the highest-ROI optimization for most production deployments. It costs nothing and requires no model changes. Continuous Batching and Throughput vs. Latency Static batching processes a fixed batch of N requests together. If request 1 finishes early, the GPU waits for the rest. This is how early inference servers worked and it's expensive. Continuous batching (also called dynamic batching or in-flight batching) adds new requests to the batch as slots free up. vLLM, TGI, and TensorRT-LLM all implement this. It's the single biggest throughput improvement over naive serving and should be non-negotiable for any production deployment. The latency-throughput tradeoff: - Low traffic: requests are served near-immediately, latency is low, GPU utilization is low - High traffic: requests queue slightly, latency increases, GPU utilization approaches 100% - Overload: queue grows unbounded — you need autoscaling or request shedding Set max batch tokens based on your latency SLA, not just throughput maximization. A batch that's 2x larger may double throughput but also double p99 latency. Measure both. Serving Stack Comparison The main open-source options in 2026: vLLM - Best general-purpose choice. Continuous batching, paged attention, prefix caching, OpenAI-compatible API. - Supports most open-weight models. Tensor parallelism across multiple GPUs. - Production-ready, actively maintained. TensorRT-LLM - NVIDIA's optimized runtime. Best raw throughput on NVIDIA hardware. - More complex setup; model conversion required. Best

AI Inference Optimization

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