Quantisation
Post-training and quantisation-aware methods, outlier channels, GPTQ and AWQ, and low-bit arithmetic formats.
11concepts
104flashcards
80minutes of reading
- 01 Calibration Data for Post-Training Quantisation The few hundred kilobytes of text that decide how a 70B model rounds, why the default recipe of 128 random web sequences is a convention rather than a result, and what the evidence says about choosing better.
- 02 Quantisation Grids, Scale and Zero Point The affine map between floating point and integers, why granularity is the single most consequential choice, and how clipping and rounding errors trade against each other.
- 03 When Quantisation Does Not Make Inference Faster Weight-only quantisation buys bandwidth, not arithmetic, so its speedup is a function of batch size and phase; the arithmetic that predicts where the gain disappears, and why a kernel can be slower than fp16.
- 04 AWQ and Activation-Aware Scaling The observation that weight importance is determined by activation magnitude rather than weight magnitude, and how a per-channel rescaling protects the important weights without keeping any of them in higher precision.
- 05 GPTQ and Second-Order Weight Rounding Why rounding each weight to its nearest grid point is not the best rounding, how the Hessian of the layer reconstruction error tells you what to do instead, and what the approximations cost.
- 06 KV Cache Quantisation Why the KV cache quantises along different axes for keys and values, how that asymmetry follows from where the outliers live, and why the cache is the one tensor whose quantisation error compounds over a generation.
- 07 Low-Bit Number Formats and Microscaling Why floating-point formats at 8 bits and below split differently between exponent and mantissa, what a shared block exponent buys, and how hardware support decides which format wins regardless of its numerical merits.
- 08 Mixed-Precision Bit Allocation and Sensitivity Why a fixed bit budget is better spent unevenly across layers, how Hessian-based sensitivity from HAWQ turns an exponential search into an integer programme, and where the second-order proxy and the hardware disagree.
- 09 Quantisation and Reasoning Models Reasoning models spend thousands of tokens per answer, which is exactly the regime where small per-token errors have room to compound; what the measurements show, where they disagree, and what to evaluate.
- 10 Quantisation-Aware Training and the Straight-Through Estimator How you backpropagate through a step function that has zero gradient everywhere, what QAT buys over post-training methods, and why it is used far less than its accuracy would justify.
- 11 Quantising Mixture-of-Experts Models Why MoE models are the most attractive quantisation target and the most fragile one, how the router turns rounding error into a discrete change of computation path, and what belongs in high precision.