Inference Optimization: Pro Course — Make LLMs Faster, Cheaper, and Production-Ready
Master LLM inference optimization — quantization, speculative decoding, vLLM, KV cache, batching & hardware acceleration. Cut costs by 80% at aimodels.in.
Course Snapshot
| Course Code | AIM-610 |
| Duration | 50+ hours |
| Modules | 11 |
| Projects | 4 |
| Phase | 6 — Inference, Enterprise & Deployment |
| Skill Level | Intermediate to Advanced |
| Format | Self-paced + live cohorts |
| Price | ₹6,999 (early bird ₹4,999) |
| Last Updated | July 2026 |
Course Overview
Inference is where the cost of large language models actually lives. A model that takes 800 milliseconds to generate a single token and consumes 40GB of VRAM is not a product — it is a liability. This course is a deep, hands-on dive into every lever you can pull to make LLM inference faster, cheaper, and more reliable without sacrificing quality. You will start by understanding the fundamental bottlenecks of autoregressive generation — the prefill versus decode phases, memory bandwidth walls, and the KV cache that dominates GPU memory at scale. From there you will master quantization techniques from INT8 to INT4 to NF4, learning exactly when each method preserves quality and when it silently degrades outputs. You will optimize attention with FlashAttention, PagedAttention, and grouped-query attention, then squeeze the KV cache with paged memory and compression strategies. You will push throughput through static, dynamic, and continuous batching, and reach production-grade serving with vLLM. You will accelerate generation with speculative decoding and draft models, deploy NVIDIA-optimized inference with TensorRT-LLM and Triton, compress models with pruning and knowledge distillation, and tune every kernel for specific hardware from A100 to H100 to consumer GPUs. By the end you will have four portfolio projects, including a vLLM deployment serving 10,000 tokens per second and a quantized model that fits a 70B parameter LLM on a single GPU with under 3% quality loss. Whether you serve models internally or ship APIs to millions of users, this course gives you the complete optimization toolkit.
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Enroll Now — Limited Seats →Who This Course Is Built For
This course is designed for specific professional profiles. If you match any of these, you will get maximum value.
ML Engineers & MLOps Engineers
Engineers responsible for serving LLMs in production who need to hit latency, throughput, and cost targets without compromising model quality.
AI Infrastructure Engineers
Builders who manage GPU clusters and inference servers and need to maximize utilization, minimize waste, and squeeze every last token per second from their hardware.
Startup CTOs & Founders
Technical leaders whose LLM API bills are scaling faster than revenue and who need practical, proven techniques to cut inference costs by 60–80%.
Backend Engineers Moving Into AI
Experienced backend engineers who can build APIs but need the specialized knowledge to serve ML models efficiently under real production load.
This Course Is NOT For You If:
- Beginners who have never loaded a transformer model or served a model behind an API — start with our Hugging Face or LLMs Pro courses first.
- Those looking for a purely theoretical course — every module includes hands-on benchmarking and optimization labs.
- Anyone without access to a GPU — the labs require at least a Colab T4 or equivalent for quantization and serving exercises.
What You Will Learn
After completing this course, you will be able to:
- 1 You will be able to diagnose inference bottlenecks using profiling tools and identify whether a workload is compute-bound or memory-bound
- 2 You will be able to apply INT8, INT4, GPTQ, AWQ, and NF4 quantization and measure their quality and latency trade-offs
- 3 You will be able to optimize attention with FlashAttention-2, PagedAttention, and grouped-query attention for memory and speed
- 4 You will be able to manage the KV cache with paged memory, compression, and eviction strategies for long-context workloads
- 5 You will be able to implement static, dynamic, and continuous batching to maximize GPU throughput
- 6 You will be able to deploy and tune vLLM for high-throughput production serving with PagedAttention and continuous batching
- 7 You will be able to implement speculative decoding with draft models to achieve 2–3x latency reduction
- 8 You will be able to build NVIDIA-optimized inference pipelines with TensorRT-LLM and the Triton Inference Server
- 9 You will be able to compress models with structured pruning, knowledge distillation, and model merging
- 10 You will be able to tune inference kernels for specific hardware including A100, H100, and consumer GPUs
Download Free Course Syllabus
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Download Free Syllabus →Complete Course Curriculum
11 modules with detailed lessons. Every lesson includes specific learning points.
MODULE 1 — Inference Fundamentals & Bottlenecks
4 hours- Understand the prefill phase (prompt processing) versus the decode phase (token generation)
- Analyze why decode is memory-bandwidth-bound and prefill is compute-bound
- Trace a single forward pass through attention, MLP, and projection layers
- Measure time-to-first-token (TTFT) and inter-token latency as primary metrics
- Use PyTorch profiler, Nsight Systems, and Chrome trace to profile inference
- Identify whether a workload is compute-bound, memory-bound, or latency-bound
- Measure GPU utilization, memory bandwidth, and kernel launch overhead
- Build a benchmarking harness that tracks latency, throughput, and memory across configurations
- Calculate cost per token from GPU hourly price, throughput, and utilization
- Understand how batch size, sequence length, and model size interact in cost equations
- Compare API-based vs self-hosted cost models and break-even analysis
- Identify the top 5 cost levers: quantization, batching, caching, model size, and hardware choice
MODULE 2 — Quantization Techniques
5 hours- Understand the difference between weight-only, activation, and KV cache quantization
- Learn symmetric vs asymmetric quantization and per-channel vs per-tensor scaling
- Analyze the quality impact of INT8, INT4, and lower precision on different model layers
- Benchmark FP16 vs INT8 vs INT4 on latency, memory, and perplexity
- Apply GPTQ for post-training quantization with activation-aware weight calibration
- Use AWQ (Activation-aware Weight Quantization) for superior INT4 quality
- Compare GPTQ vs AWQ on Llama, Mistral, and Qwen models for quality and speed
- Convert and serve GPTQ and AWQ models with vLLM and ExLlamaV
- Use bitsandbytes for on-the-fly INT8 and INT4 loading of any Hugging Face model
- Understand NF4 (NormalFloat 4-bit) and double quantization from the QLoRA paper
- Benchmark bitsandbytes vs GPTQ vs AWQ for inference quality and speed
- Choose the right quantization method for your deployment target and quality requirements
MODULE 3 — Attention Optimisation
4 hours- Understand the IO-aware tiling strategy that makes FlashAttention faster without approximation
- Benchmark FlashAttention-2 vs standard attention on latency and memory
- Configure FlashAttention in vLLM, TGI, and Hugging Face Transformers
- Identify when FlashAttention helps most: long sequences, large batch sizes, and training
- Understand PagedAttention as virtual memory for the KV cache, eliminating fragmentation
- Compare PagedAttention vs contiguous KV cache allocation in memory utilization
- Enable PagedAttention in vLLM and measure memory savings on long-context workloads
- Handle variable-length sequences and beam search with paged KV cache
- Understand how GQA and MQA reduce KV cache size by sharing key and value heads
- Convert a multi-head attention model to GQA for inference efficiency
- Benchmark MHA vs GQA vs MQA on latency, memory, and quality
- Identify which modern models (Llama 3, Mistral) use GQA and why it matters for serving
MODULE 4 — KV Cache Optimisation
4 hours- Calculate KV cache size from model dimensions, layers, and sequence length
- Understand why KV cache dominates GPU memory at scale and for long contexts
- Profile KV cache allocation and identify fragmentation and waste
- Measure the impact of context length on maximum concurrent requests
- Apply KV cache quantization to INT8 or INT4 for 2–4x memory reduction
- Implement sliding window and token eviction for long-context generation
- Use H2O (Heavy-Hitter Oracle) and streaming attention for cache management
- Benchmark quality impact of cache compression on long-document tasks
- Understand prefix caching for repeated system prompts and few-shot examples
- Enable automatic prefix caching in vLLM for multi-turn and template workloads
- Implement manual prompt caching with hash-based cache keys
- Measure latency and cost savings on workloads with shared prompt prefixes
MODULE 5 — Batching & Throughput
5 hours- Understand static batching: fixed batch size, simple but inflexible
- Learn dynamic batching: request queueing with timeout-based batch formation
- Master continuous batching (iteration-level scheduling) for maximum GPU utilization
- Benchmark throughput across all three strategies on the same hardware
- Understand iteration-level scheduling and why it eliminates head-of-line blocking
- Configure continuous batching in vLLM and TGI for mixed-length workloads
- Measure throughput gains of 5–10x over static batching on real traffic patterns
- Handle preemption and backpressure when GPU memory is exhausted
- Tune max batch size, max num seqs, and GPU memory utilization for your workload
- Calculate maximum concurrent requests from KV cache and model memory budgets
- Design auto-scaling policies based on queue depth and token throughput
- Benchmark and capacity-plan for peak vs average load on A100 and H100
MODULE 6 — vLLM Mastery
5 hours- Understand vLLM architecture: scheduler, workers, PagedAttention, and continuous batching
- Install and configure vLLM for local and distributed serving
- Load models from Hugging Face Hub, local checkpoints, and quantized formats
- Benchmark vLLM vs Hugging Face Transformers vs TGI on throughput and latency
- Configure the OpenAI-compatible API server for drop-in replacement of OpenAI
- Set up tensor parallelism and pipeline parallelism for multi-GPU serving
- Enable and tune prefix caching, chunked prefill, and speculative decoding
- Implement quantized serving with AWQ, GPTQ, and bitsandbytes in vLLM
- Deploy vLLM behind a load balancer with health checks and graceful shutdown
- Monitor vLLM with Prometheus metrics: throughput, queue depth, and GPU utilization
- Handle model swapping and multi-model serving on shared GPUs
- Troubleshoot common vLLM issues: OOM, startup failures, and throughput drops
MODULE 7 — Speculative Decoding
4 hours- Understand how a small draft model proposes tokens that a large verifier accepts or rejects
- Analyze the speedup math: why speculative decoding reduces latency without quality loss
- Compare lossless speculative decoding with lossy variants like Medusa and EAGLE
- Identify workloads where speculative decoding helps most: short sequences and high accept rates
- Configure speculative decoding in vLLM with a draft model
- Use Medusa heads and EAGLE for head-based speculative decoding without a separate model
- Benchmark speculative decoding speedup on Llama and Mistral models
- Tune draft model size and number of speculative tokens for optimal accept rate
- Use n-gram and lookup-based speculative decoding for zero-cost acceleration
- Implement speculative decoding for code models with grammar-constrained generation
- Combine speculative decoding with quantization and batching for compound speedups
- Profile accept rates and identify when speculative decoding hurts throughput
MODULE 8 — TensorRT-LLM & Triton
5 hours- Understand TensorRT-LLM as NVIDIA's optimized inference engine for LLMs
- Build TensorRT-LLM engines from Hugging Face checkpoints with the build script
- Configure INT8 and FP8 quantization with SmoothQuant and weight-only quantization
- Benchmark TensorRT-LLM vs vLLM on A100 and H100 for latency and throughput
- Deploy TensorRT-LLM engines behind the Triton Inference Server for production serving
- Configure model repositories, dynamic batching, and instance groups in Triton
- Set up multi-model serving with GPU sharing and model swapping
- Monitor Triton with Prometheus metrics and Grafana dashboards
- Leverage H100-specific features: FP8, transformer engine, and fused kernels
- Use NVIDIA's NIM (NVIDIA Inference Microservice) for containerized deployment
- Benchmark FP8 vs INT8 vs FP16 on H100 for quality and speed
- Optimize kernel configurations for specific NVIDIA GPU architectures
MODULE 9 — Model Compression & Pruning
4 hours- Understand structured pruning (removing heads, layers) vs unstructured (removing weights)
- Apply magnitude pruning and movement pruning to LLMs
- Benchmark quality impact of pruning 10%, 20%, and 30% of model weights
- Use Wanda and SparseGPT for LLM-specific pruning without retraining
- Distill a large teacher model into a smaller student model for faster inference
- Configure distillation with logit matching and hidden state transfer
- Benchmark student model quality vs the teacher on standard benchmarks
- Combine distillation with quantization for compound compression
- Use SLERP, DARE, and task arithmetic to merge models without additional training
- Build a full compression pipeline: prune, distill, quantize, and serve
- Benchmark the final compressed model against the original on quality and speed
- Document compression trade-offs for stakeholders and decision-making
MODULE 10 — Hardware-Specific Optimisation
5 hours- Understand A100 vs H100 vs L40 vs consumer GPUs (RTX 4090) for inference
- Analyze memory bandwidth, compute throughput, and interconnect (NVLink) differences
- Choose the right GPU for your workload: latency-sensitive vs throughput-optimized
- Calculate cost-per-token on different hardware including cloud vs on-premise
- Configure tensor parallelism for models too large for one GPU
- Use pipeline parallelism for multi-GPU serving with vLLM and TensorRT-LLM
- Benchmark TP vs PP vs DP for different model sizes and cluster configurations
- Handle NCCL communication overhead and optimize for multi-node inference
- Deploy models with llama.cpp and GGUF format for CPU and edge inference
- Use Apple Silicon (MLX) for on-device inference on Mac
- Benchmark CPU inference with OpenVINO and ONNX Runtime
- Optimize for edge devices: ARM, mobile, and embedded inference constraints
MODULE 11 — Projects
4 hours- Quantize a 7B model with GPTQ and AWQ and benchmark quality vs speed
- Deploy the quantized model with vLLM and measure throughput gains
- Document the quality-latency trade-off curve for stakeholders
- Present your deployment with benchmark dashboards and cost analysis
- Configure vLLM with continuous batching, prefix caching, and tensor parallelism
- Benchmark against a baseline Hugging Face serving setup
- Achieve a target of 10,000+ tokens per second on available hardware
- Build a monitoring dashboard with Prometheus and Grafana
- Implement speculative decoding with a draft model for a target LLM
- Benchmark latency reduction and measure accept rates across prompt types
- Compare Medusa and EAGLE approaches against standard speculative decoding
- Document when speculative decoding helps and when it hurts
- Apply pruning, quantization, and kernel tuning to a single model end-to-end
- Benchmark the fully optimized model against the original baseline
- Target 80% cost reduction while maintaining under 5% quality loss
- Present a production deployment guide with all optimization decisions documented
Real-World Projects & Portfolio Outcomes
4 portfolio-worthy projects that prove your skills to employers.
Quantized Model Deployment
Quantize a 7B parameter LLM with both GPTQ and AWQ, benchmark quality and speed against the FP16 baseline, and deploy the best variant with vLLM. Document the full quality-latency-cost trade-off curve.
vLLM High-Throughput Server
Build a production vLLM serving stack with continuous batching, prefix caching, and tensor parallelism. Benchmark against a baseline and achieve 10,000+ tokens per second on available hardware.
Speculative Decoding Accelerator
Implement speculative decoding with a draft model for a target LLM, benchmark latency reduction across different prompt types, and compare Medusa and EAGLE approaches against standard speculative decoding.
Full Optimization Pipeline
Apply pruning, quantization, and kernel tuning to a single model end-to-end. Target 80% cost reduction while maintaining under 5% quality loss. Present a production deployment guide with all decisions documented.
Tools, Technologies & Models Covered
LLM Models Covered
| Llama 3.1 8B | Meta | Quantization, vLLM, speculative decoding |
|---|---|---|
| Mistral 7B | Mistral AI | GQA, pruning, distillation |
| Qwen 2.5 7B | Alibaba | AWQ, GPTQ, edge deployment |
| Llama 3.1 70B | Meta | Multi-GPU, TensorRT-LLM, FP8 |
| Phi-3 Mini | Microsoft | CPU inference, edge optimization |
| Gemma 2 9B | KV cache, attention optimization |
Tools & Frameworks Covered
| vLLM | High-throughput serving with PagedAttention | Deep |
|---|---|---|
| TensorRT-LLM | NVIDIA-optimized inference engine | Deep |
| Triton Inference Server | Production model serving | Intermediate |
| bitsandbytes | On-the-fly INT8 and INT4 quantization | Deep |
| AutoGPTQ | GPTQ post-training quantization | Intermediate |
| AutoAWQ | AWQ activation-aware quantization | Intermediate |
| llama.cpp | CPU and edge inference with GGUF | Intermediate |
| PyTorch Profiler | Inference profiling and bottleneck diagnosis | Intermediate |
| Nsight Systems | NVIDIA GPU profiling | Intermediate |
Book a Free Counselling Call
Not sure which course is right for you? Book a free 1:1 counselling call with our AI training advisors.
Book Free Counselling Call →Your Instructor
Dr. Arjun Mehta
Lead Inference Engineer & ML Systems Architect
8+ years in ML infrastructure and inference optimization
Optimized inference for models serving 10M+ daily requests
3,200+ engineers trained
Inference is engineering, not magic. Every millisecond and every megabyte has a reason. Understand the bottleneck, apply the right technique, measure the result, and iterate. A model that is not measured is not optimized.
Course Team & Curriculum Design
Our instructor team includes former ML infrastructure engineers from leading AI companies who have served models at million-request scale. Teaching assistants are available for benchmarking and deployment support during live cohorts.
Learning Path & Prerequisites
Prerequisites
- Intermediate Python and basic PyTorch (tensors, model loading, forward passes)
- Familiarity with Hugging Face Transformers (load model, tokenizer, generate)
- Understanding of transformer architecture (attention, MLP, layer norm)
- Access to a GPU with at least 16GB VRAM (Colab, Kaggle, or local)
- Completion of AIM-606 (LLMs Pro) or equivalent experience recommended
Recommended Learning Paths
ML Engineer optimizing production serving
- Complete Modules 1–5 for fundamentals, quantization, attention, KV cache, and batching
- Deep-dive Module 6 for vLLM mastery and production deployment
- Use Modules 7–8 for speculative decoding and TensorRT-LLM acceleration
- Build all 4 projects for a complete optimization portfolio
Infrastructure engineer maximizing GPU utilization
- Focus on Modules 1, 5, 6, and 10 for batching, vLLM, and hardware tuning
- Use Module 8 for NVIDIA-specific optimizations on A100 and H100
- Complete the vLLM High-Throughput Server and Full Optimization Pipeline projects
Startup CTO cutting inference costs
- Take Modules 1–2 for cost model and quantization fundamentals
- Use Module 6 for vLLM deployment and Module 9 for model compression
- Complete the Quantized Model Deployment project for immediate cost savings
What Comes After This Course
Pricing & Enrollment
Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.
| Self-Paced | ₹6,999 | All 11 modules, 4 projects, community Discord, lifetime access |
|---|---|---|
| Early Bird | ₹4,999 | Same as Self-Paced — limited time before cohort launch |
| Cohort Live | ₹12,999 | Live sessions, TA support, code review, and certificate |
What Is Included
- 11 modules with 35+ hands-on benchmarking and optimization labs
- 4 capstone projects with code review and feedback
- Pre-configured Colab and Kaggle notebooks for every lab
- Private Discord community with instructors and TAs
- Lifetime access including future updates as inference tech evolves
- Certificate of completion with project portfolio
30-day money-back guarantee. If you complete the first 4 modules and feel the course is not for you, get a full refund — no questions asked.
Frequently Asked Questions
Do I need my own GPU to take this course?
No. Every lab includes a pre-configured Google Colab notebook that runs on a free T4 GPU. For larger models (70B), we provide RunPod and Modal templates with hourly pricing. If you have a local GPU with 16GB+ VRAM, you can run everything locally for faster iteration.
What is the difference between GPTQ, AWQ, and bitsandbytes?
GPTQ and AWQ are post-training quantization methods that produce pre-quantized model files optimized for inference. bitsandbytes loads any model and quantizes on the fly, which is more flexible but slower for inference. GPTQ and AWQ generally give better speed and quality at INT4, while bitsandbytes is easiest for experimentation. We cover all three in Module 2.
How much cost reduction can I realistically achieve?
With quantization (INT4), continuous batching, and prefix caching, most teams achieve 60–80% cost reduction. The exact number depends on your workload — short prompts with high repetition benefit most from caching, while long-context workloads benefit most from KV cache optimization. The Full Optimization Pipeline project targets 80% reduction with under 5% quality loss.
Is speculative decoding worth the complexity?
For latency-sensitive workloads with high accept rates (short outputs, code generation, chat), speculative decoding delivers 2–3x latency reduction with no quality loss. For throughput-optimized batch workloads, it can actually hurt. Module 7 covers when to use it, how to configure it, and how to measure whether it helps your specific workload.
Should I use vLLM or TensorRT-LLM?
vLLM is easier to set up, works across hardware, and is the best default for most teams. TensorRT-LLM delivers the best raw performance on NVIDIA GPUs but requires more setup and NVIDIA-specific tooling. Module 6 covers vLLM in depth and Module 8 covers TensorRT-LLM, so you can make an informed choice for your infrastructure.
Will I get a certificate?
Yes. Complete all 4 capstone projects and submit them for code review. Upon passing, you receive a certificate with your project portfolio links, suitable for sharing on LinkedIn and with employers.
How is this course different from the Model Serving course (AIM-612)?
This course (AIM-610) focuses on making inference faster and cheaper — quantization, attention, KV cache, batching, and speculative decoding. The Model Serving course (AIM-612) focuses on deployment infrastructure — Docker, Kubernetes, cloud platforms, and production reliability. They complement each other: this course optimizes the engine, the serving course builds the vehicle around it.
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