Topic
Speculative decoding, KV cache, tensor parallelism, batching strategies, and the systems that serve LLMs at scale.
18 posts
General Compute will bring Cerebras wafer-scale inference to its customers at scale. Capacity opens in Q1 2027, and agentic coding is the first workload. Here is what we are doing and why.
A practical guide to LLM quantization: what INT4, GGUF, and GPTQ actually do, how much quality you lose, and how to quantize a model yourself with llama.cpp and AutoGPTQ.
How modern ML compilers turn Python model code into fused, fast kernels. A practical look at TorchInductor, Triton, and XLA, and the tradeoffs each one makes for inference.
Picking a draft model is the most consequential decision when deploying speculative decoding. A practical guide to acceptance rates, sizing, and the tradeoffs that decide whether you actually get a speedup.
How tensor and pipeline parallelism actually differ in production inference, when to use each, and why most serving stacks end up combining them.
How prefix caching works in modern LLM serving stacks, why it changes the economics of long system prompts and RAG, and what to watch out for in production.
Batching is the lever that turns idle GPU silicon into served tokens. This post walks through the evolution of batching for LLM serving, from one-at-a-time to static batches to request-level dynamic batching to iteration-level continuous batching, and shows where each strategy still leaves throughput on the floor.
In LLM serving, a single long-running request can stall everyone else sharing the same batch. S3 attacks that by predicting output length and scheduling around it. Here is what stragglers actually cost you, and how output-length-aware scheduling helps.
Prefill pins the compute units while decode starves for memory bandwidth. Sarathi-Serve splits prefill into chunks and piggybacks decodes on them, keeping both resources busy in the same batch. Here is how it works and where the limits are.
FrugalGPT and its descendants show that most queries do not need the biggest model. We walk through the cascade pattern, routing classifiers, and the engineering trade-offs of sending easy work to cheap models and escalating only when needed.
Lookahead decoding from LMSYS speeds up autoregressive generation without requiring a draft model. We walk through the Jacobi iteration trick, the n-gram pool, and what the speedups actually look like in practice.
Prefill and decode have different compute profiles and clash when they share a GPU. Splitwise and DistServe separate them onto different hardware pools. We walk through why, how, and when it actually pays off.
DeepSeek's Multi-Head Latent Attention cuts the KV cache by an order of magnitude without giving up quality. We walk through MLA, how it compares to MQA and GQA, and the other compression techniques worth knowing.
MQA and GQA reduce the memory footprint of attention by sharing key-value heads across queries. A simple architectural change that makes inference dramatically faster.
Before continuous batching, LLM servers wasted GPU cycles waiting for the slowest request in each batch. Orca's iteration-level scheduling fixed this with a 36x throughput improvement.
The original speculative decoding papers needed a separate draft model. Medusa, EAGLE, and Sequoia found ways to speculate faster, smarter, and without the extra model.
SGLang's RadixAttention stores KV cache in a radix tree, enabling automatic prefix sharing across requests. The result is up to 5x higher throughput for multi-turn and structured workloads.
Speculative decoding uses a small draft model to predict multiple tokens ahead, then verifies them all at once. The result is mathematically identical output, 2-3x faster.