Building High-Performance LLM Inference Engines in C

From Transformer Fundamentals to a Complete SafeTensors Inference Runtime

Introduction: How LLMs Actually Generate Text

Chapter 1: How LLMs Think: The Transformer Inference Journey

1. The Inference Journey from Prompt to Response
2. Why Inference Is Different from Training
3. The Transformer Blueprint at a Glance
4. What This Book Builds
5. A First Look: Loading, Running and Generating

Chapter 2: Tokens, Vocabularies and the Language of Models

1. What a Token Really Is
2. Byte Pair Encoding and Subword Tokenization
3. Loading and Interpreting Tokenizer Files
4. Implementing Token Encoding in C
5. Implementing Token Decoding and Special Tokens

Chapter 3: Tensors, Shapes and Memory Layouts

1. The Tensor as a Multi-Dimensional Array
2. Row-Major vs Column-Major and Why It Matters
3. Shapes, Strides and Offsets
4. Float16, BFloat16 and Float32 Representations
5. Contiguous vs Strided vs Packed Tensors

Chapter 4: Building the Tensor Core Library

1. Tensor Allocation and Lifetime Management
2. Views, Reshaping and Slicing Without Copies
3. Element-Wise Operations and Broadcasting
4. Matrix Multiplication from First Principles
5. Reductions, Sums and Normalization Primitives

Chapter 5: The Transformer Layer in Depth

1. Layer Normalization and RMSNorm
2. Multi-Head Self-Attention Mechanism
3. Feed-Forward Networks and Activation Functions
4. Residual Connections and Gate Linear Units
5. Putting It All Together: One Transformer Block

Chapter 6: Positional Encodings and RoPE

1. Why Positional Information Matters
2. Learnable vs Absolute vs Rotary Encodings
3. The Mathematics of RoPE
4. Implementing RoPE in C
5. RoPE Scaling and Long-Context Strategies

Chapter 7: Softmax, Logits and Sampling

1. From Hidden States to Logits
2. Numerically Stable Softmax
3. Temperature Scaling and Exploration
4. Top-K Filtering and Nucleus Sampling
5. Deterministic and Reproducible Generation

Chapter 8: The KV Cache and Autoregressive Generation

1. The Autoregressive Generation Loop
2. Key-Value Caching: The Core Optimization
3. KV Cache Layouts and Memory Planning
4. Prefill vs Decode Phases
5. Managing Cache Growth and Limits

Chapter 9: The SafeTensors File Format

1. Why SafeTensors and What Problem It Solves
2. Header Structure and Serialization
3. Tensor Descriptors, Data Types and Offsets
4. Endianness, Alignment and File Boundaries
5. Supported Data Types and Shape Encoding

Chapter 10: Implementing the SafeTensors Loader in C

1. Header Reading and Length Prefix Parsing
2. JSON Metadata Parsing in C
3. Tensor Descriptor Extraction and Validation
4. Mapping and Reading Tensor Data Blocks
5. Error Handling, Boundaries and Integrity Checks

Chapter 11: Model Configuration and Weight Mapping

1. The config.json: Architecture Parameters
2. Weight Naming Conventions and Patterns
3. Mapping Transformer Layer Weights
4. Handling Embeddings and Output Projections
5. Validation: Checking Loaded Weight Shapes

Chapter 12: Building the Model Forward Pass

1. Token Embedding Lookup
2. The Layer Loop: Attention, FFN and Residuals
3. Positional Encoding at Each Position
4. Final Layer Normalization
5. Computing Output Logits
6. The Complete Attention Implementation
7. Putting It All Together: The Forward Pass

Chapter 13: End-to-End Inference: From Prompt to Text

1. Runtime State and Context Management
2. The Generation Loop Implementation
3. Integration: Tokenizer, Model and Sampler
4. Streaming Output and Callbacks
5. Command-Line Interface and First Run

Chapter 14: Verifying Correctness Before Optimizing

1. Reference Implementations and Ground Truth
2. Comparing Embeddings and Intermediate Activations
3. Logit Comparison and Numerical Tolerance
4. Generating Test Cases from Known Outputs
5. Assertions, Sanitizers and Debugging Techniques

Chapter 15: Performance Profiling and Bottleneck Analysis

1. Latency, Throughput and Tokens Per Second
2. Profiling Tools: Perf, Valgrind and Flame Graphs
3. Compute-Bound vs Memory-Bound Analysis
4. Cache Misses, Branch Mispredictions and Latency
5. Identifying the Critical Path

Chapter 16: SIMD and Vectorization

1. Understanding SIMD Architectures: AVX2, AVX-512, NEON
2. Autovectorization vs Hand-Written Intrinsics
3. Vectorized Element-Wise Operations
4. Vectorized Matrix Multiplication
5. Cross-Platform SIMD Portability

Chapter 17: Memory and Threading Optimizations

1. Cache Locality and Data Layout Optimization
2. Loop Tiling and Blocking for MatMul
3. Multithreading with OpenMP and Native Threads
4. False Sharing, Synchronization and Contention
5. NUMA Awareness and Large Memory Models
6. Memory Bandwidth, Compute-Bound vs Memory-Bound Workloads

Chapter 18: Advanced Optimization Techniques

1. Weight Quantization and Low-Bit Representations
2. Packed Weights and Efficient Memory Use
3. Batching and Continuous Batching
4. Speculative Decoding and Draft Models
5. Asynchronous Execution and Pipeline Parallelism
6. Model-Size-Dependent Optimization Strategies

Conclusion: What We Built and Why It Matters

References