COURSE · OPS · A012

Custom Quantization and Kernels

Learn custom quantization and kernels through RunLocalAI's practical lens: quantization, cuda, kernels and tensorrt, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.

18 chapters16hOperator trackBy Fredoline Eruo
PREREQUISITES
  • I016

Why this course matters

Custom Quantization and Kernels is for operators making local AI reliable, measurable and cheaper to run. It connects quantization, cuda, kernels, tensorrt and benchmarking to the questions RunLocalAI wants every reader to answer before they install, upgrade or scale a model: will it run, what will it cost in memory, what setting changes the result, and how do you verify the answer instead of trusting a demo?

What you will be able to do

By the end, you should be able to explain the main tradeoffs in plain language, choose a safe next experiment, and use the chapter exercises as a repeatable operator checklist. The course favors local evidence, hardware fit, context limits, latency and failure modes over generic AI vocabulary.

How to use this course

Start at chapter one if the topic is new. If you already have a working stack, scan for chapters such as Quantization Theory, Weight Quantization, Activation Quantization and Calibration Datasets and use those lessons as a quality-control pass before changing a workstation, team workflow or production-like local deployment.

CHAPTERS
  1. 01Quantization TheoryQuantization is fundamentally a lossy compression problem where scale and zero-point parameters control the mapping between continuous float values and discrete integer representations.15 min
  2. 02Weight QuantizationPer-channel weight quantization captures the natural variation in filter magnitudes across neural network layers, making it the preferred approach for most modern model architectures.20 min
  3. 03Activation QuantizationActivation quantization requires balancing runtime flexibility against computational overhead, with techniques like smoothquant redistributing the quantization difficulty from activations to easier-to-quantize weights.20 min
  4. 04Calibration DatasetsCalibration dataset quality determines quantization accuracy. The dataset must statistically represent production inference conditions including domain, length distribution, and vocabulary characteristics.20 min
  5. 05GGUF Format Deep DiveGGUF's explicit type system and self-describing tensor structure enable inference engines to correctly interpret any supported quantization format without external configuration files.20 min
  6. 06Custom Quant SchemesCustom quantization schemes emerge from co-designing the storage representation and inference kernels as an integrated system, with the computational efficiency of the dequantization path determining practical utility.20 min
  7. 07Mixed PrecisionMixed precision succeeds by identifying which model components dominate accuracy and targeting high-precision compute there while aggressively quantizing components where quality tolerance is higher—particularly caches and embedding layers.20 min
  8. 08CUDA Kernel BasicsEfficient CUDA kernels for quantized inference balance memory coalescing requirements—loading contiguous data—against the frequently fragmented nature of quantized weight representations through careful block-level organization.20 min
  9. 09Memory CoalescingQuantized data layouts sacrifice natural spatial locality to gain compression, placing the burden on kernel designers to restructure computation into coalesced patterns, typically through shared memory tiling stages.25 min
  10. 10Kernel OptimizationKernel optimization follows diminishing returns—measure before optimizing, and focus on the dominant bottleneck (memory bandwidth, instruction throughput, or latency hiding).15 min
  11. 11TensorRT Plugin DevelopmentPlugin serialization must capture all state needed for reconstruction. Use versioning to handle schema evolution gracefully.15 min
  12. 12INT8 GEMMDequantization overhead can be significant for small matrices. Fuse dequantization with downstream operations or use table lookups for common scale combinations.15 min
  13. 13FP8 InferenceFP8 requires calibration similar to INT8, but the format's native range handling often reduces sensitivity to outlier values in transformer weights.15 min
  14. 14Kernel BenchmarkingLatency and throughput measurements require different batch sizes. Use single-invocation timing for latency-critical paths; use batched runs for throughput analysis.15 min
  15. 15Quantization AccuracyPer-channel quantization with careful calibration achieves accuracy comparable to floating-point for most models, but requires careful handling of outlier channels.15 min
  16. 16Deploying Custom KernelsVersion control for kernels is critical—ensure CUDA version compatibility and provide fallback implementations for unsupported architectures.20 min
  17. 17Integration with RuntimesRuntime integration requires careful memory management. Use preallocated buffers and avoid tensor copies in hot paths.15 min
  18. 18Custom Quantization ProjectEnd-to-end quantization requires careful attention to the error budget—each layer's quantization error accumulates through the network. Calibration with representative data is essential for production accuracy.25 min
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