Inference optimization is the discipline of running trained AI models efficiently in production — quantization, batching, and sampling techniques that trade compute, latency, and quality against cost.
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Each topic below is a key concept in this domain. Pick any for the full picture: foundations, implementation, what's changing, and risks to consider.
Continuous batching is a serving optimization for large language models that dynamically groups inference requests and …
Inference is the process of running a trained machine learning model to generate predictions, classifications, or text …
Quantization is the process of reducing the numerical precision of a neural network's weights and activations, for …
Temperature and sampling are the parameters that control how a large language model selects its next token during text …
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Updated Mar 26, 2026
Concepts covered
Continuous batching swaps finished LLM requests every decode step. Learn how PagedAttention cuts KV cache waste to under 4% and where the hard limits emerge.
Continuous batching replaces request-level scheduling with iteration-level scheduling, keeping GPUs busy on every forward pass. Learn how the mechanism works.
Sub-4-bit quantization promises smaller LLMs, but accuracy collapses unevenly across tasks and languages. Learn where the real degradation thresholds are.
GPTQ, AWQ, GGUF, and bitsandbytes each shrink LLM weights differently. Compare speed, accuracy, and hardware reach to find the right format for your inference stack.
Wrong sampling parameters trap LLMs in repetition loops or hallucination. Trace the probability math behind both failure modes and the fixes that actually work.
KV-cache, PagedAttention, and continuous batching form the inference pipeline core. Learn how memory management determines your LLM latency and cost.
LLM inference hits hard physical walls — memory, quadratic attention, bandwidth. Learn the engineering limits and 2026 workarounds shaping real-world AI costs.
Model inference generates LLM text one token at a time via autoregressive decoding. Learn why this sequential bottleneck shapes every optimization in modern AI serving.
Compare top-k, top-p, min-p, and beam search LLM sampling methods. Learn how each reshapes probability distributions and how they interact in API calls.
Quantization compresses LLM weights from FP32 to INT4, cutting memory up to 8x. Learn how GPTQ, AWQ, and calibration methods preserve accuracy on consumer GPUs.
Temperature divides logits before softmax, reshaping the token probability distribution. Learn how this parameter, top-p, and min-p control LLM randomness.
