OPEN LINK ↗
// 1h agoTUTORIAL
Modular’s LLM Handbook Maps Inference Engineering
Modular’s free LLM Inference Handbook organizes the concepts, tools, and measurement techniques needed to serve models faster, cheaper, and at scale. The accompanying resource list extends the path from fundamentals through batching, quantization, GPU kernels, serving engines, and benchmarking.
// ANALYSIS
This is one of the strongest starting points for developers moving from “running a model” to engineering a production inference system.
- –Builds a practical vocabulary around prefill, decode, KV cache, latency, throughput, and goodput
- –Connects theory to systems such as vLLM, SGLang, Triton, and GPU programming tutorials
- –Covers the optimization levers that matter most in production: continuous batching, PagedAttention, prefix caching, quantization, and speculative decoding
- –Interactive visualizers and calculators make abstract memory and latency trade-offs easier to reason about
- –Treat its formulas and benchmarks as starting points, then validate them against your model, hardware, and traffic patterns
// TAGS
llm-inference-handbookllminferencegpuquantizationopen-sourcedevtoolbenchmarking
DISCOVERED
1h ago
2026-10-17
PUBLISHED
1h ago
2026-10-17
RELEVANCE
9/ 10
AUTHOR
techNmak
