01 Chapter / 8
Define the Workload Before Deriving Inference Costs
If two implementations both claim more tokens per second, how do we check that they use the same workload and time denominator?
Learning Objectives
- Create the request timeline and distinguish model calculation from user waiting
- Multiplier cost per item for prefilling and decoding
- Applicable conditions for the estimation of costs by unit and reverse inspection
02 Chapter / 8
KV Caching: Verify Reuse, Positions and Windows
After retaining previous keys and values, how do we prove that chunked computation equals a full-prefix forward pass?
Learning Objectives
- Use causal dependence to prove the legitimacy of multilayer caches.
- Enable and verify global causal position of multi-word widgets
- Use the error prefix to overflow the window to determine the cache boundary
03 Chapter / 8
From KV Elements to Memory Accounting and Admission Budgets
Given layers, batch size and sequence length, how do we calculate cache memory and request capacity before allocation?
Learning Objectives
- Activate caches, weights, temporarily activated stand-alone space books
- Compute project and cache differences between MHA and GQA
- Set access conditions with page-by-page debris and safe balances
04 Chapter / 8
Continuous Batching: State, Timelines and Falsifiable Simulations
When a short request arrives before a long one finishes, how does continuous batching change waiting and completion times?
Learning Objectives
- Retroactive FIFO discrete dispatch and check for compliance
- Calculated completion, waiting and utilization of static versus successive batches
- Identifying boundaries between simulation clocks and real hardware, first words, target
05 Chapter / 8
Quantization: From Rounding Errors to Model-quality Limits
After rounding weights to a small set of levels, how do we estimate errors and distinguish fake quantization from real integer execution?
Learning Objectives
- The scale, error boundary and channel particle size for deriving symmetric quantification
- Check values risk by segregation and small spacing
- Distinguishing false quantitative error experiments, compressed storage and integer kernels
06 Chapter / 8
Verify Exact Execution and Approximate Quality Separately
Why do small differences in cached and quantized outputs require different verification criteria?
Learning Objectives
- Creates a reliance on three levels of mathematical, numerical and behavioural examination
- Distinguishing effective check-and-empt operations with positive-negative contrasts
- No-spill Quality Agreement for Designing Real Language Model Migration
07 Chapter / 8
Measurement: Fixed Traces, Paired Repeats and Performance Limits
Is one faster timing enough? How do we check numerical equivalence and repeated run times on the same inputs?
Learning Objectives
- CPU benchmarks that can be recovered and time-bound
- Recapitulate median from the original and check noise
- Distinguishing physical, sub-theory, simulations and not yet operational items
08 Chapter / 8
Final Project: Joint Evidence from One Frozen Build
How can one report combine theoretical costs, simulated scheduling and real CPU measurements without confusing their definitions?
Learning Objectives
- Resume independent experiments from an empty directory and check the source
- Distinguishing passage, failure and unverified by a joint threshold
- Preparation of a complete reasoning report on the non-crossing of the evidentiary boundary
Efficient LLM Inference
Use measured latency, throughput, and memory to guide inference optimization.
Advanced · 8 lessons · Approx. 32 hours
Scope and Experimental Limits
Eight lessons start with request timelines and multiply–add accounting, prove valid multilayer cache reuse, handle absolute-position windows, and account for real cache bytes, grouped-query attention, and paged admission. A self-contained CPU project validates discrete scheduling, floating-point quantization error, and paired timing separately using controls, raw repetitions, and source hashes. No pretrained model is downloaded. The limits of random fixtures, integer kernels, GPU tests, and live serving are stated explicitly.
Use a reproducible CPU inference lab to derive prefill and decoding costs, validate KV caching, budgets, continuous batching, and quantization error, and distinguish correctness, memory savings, and speed with raw measurements.
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Course Details
Course Contents
01 / Define the Workload Before Deriving Inference Costs02 / KV Caching: Verify Reuse, Positions and Windows
03 / From KV Elements to Memory Accounting and Admission Budgets
04 / Continuous Batching: State, Timelines and Falsifiable Simulations
05 / Quantization: From Rounding Errors to Model-quality Limits
All 8 lessons ↗
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