LLM Inference Optimization and Cost Engineering Corporate Training
Give your engineers the power to serve LLMs faster, cheaper, and at production scale.
Build the inference performance skills your engineering team needs. Edstellar's LLM Inference Optimization & Cost Engineering corporate training program helps your ML engineers master inference tuning, latency reduction, and cost-efficient serving through instructor-led sessions with hands-on labs. As the corporate training company enterprises trust for emerging-technology programs, we tailor every workshop to your stack, workloads, and goals.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build inference-efficient AI teams
THE EDSTELLAR LLM INFERENCE CAPABILITY MODEL
Measure the baseline
Profile latency, throughput, and cost on your current serving setup, then set clear targets for tuning.
Set one serving standard
One shared playbook for batching, quantization, and scheduling across every LLM your teams serve.
Tune on real workloads
Hands-on labs on realistic inference workloads turn tuning theory into repeatable practice.
Prove the cost lift
Re-benchmark latency and cost per million tokens so leadership sees the gain as a number.
Scale across products
Roll one optimized serving pattern across products and regions as your LLM usage grows.
Need more than one program?Get a group quote
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Program Overview: What is LLM Inference Optimization and Cost Engineering Corporate Training?
LLM Inference Optimization & Cost Engineering is a 16-hour instructor-led program for ML engineers and platform teams serving large language models in production. It covers the inference stack, latency and throughput profiling, quantization and compression, serving engines, and scaling strategy with cost engineering, so teams can serve models fast without overspending.
Learners profile real inference workloads, apply quantization, tune serving engines, and compare deployment configurations in hands-on labs with measurable before-and-after numbers. Guided exercises reproduce the exact decisions production teams face: batching, caching, and scaling knobs. Instructors review each optimization pass, coaching participants on the latency-cost-quality triangle. The program ends with the Cost-Optimized Deployment capstone, where learners ship a tuned serving setup with a documented cost model.
LLM inference bills shock finance teams, and slow responses lose users, yet most teams deploy with default settings and hope. Unoptimized serving wastes GPUs and budgets alike. This program teaches participants to measure what matters, optimize deliberately, and run inference infrastructure that is fast, reliable, and cost-aware.
In the Cost-Optimized Deployment capstone, each learner takes a baseline LLM serving setup, profiles it, applies quantization and serving-engine tuning, and delivers a deployment with quantified latency, throughput, and cost improvements. The result is a working optimization playbook they can rerun every time model or traffic changes.
Across six modules, your team moves from inference fundamentals to a production-grade, cost-optimized deployment. Labs use realistic workloads, profiling tools, and serving engines, so every technique transfers directly to your models.
Delivered as hybrid instructor-led training, the program fits around your releases, with scheduling set along with you, and content tailored to your stack and inference goals.
What's Included in this Workshop for Your Enterprise Teams
- 1
Inference stack foundations
Decoding, batching, and serving basics.
- 2
Quantization and model compression
Shrink models for cheaper, faster serving.
- 3
Latency and throughput labs
Profile and tune real inference workloads.
- 4
Cost engineering playbook
Model GPU economics and control inference spend.
- 5
Deployment capstone
Ship a cost-optimized serving setup in the final lab.
- Training delivered at any of your offices globally
- 10+ languages
- Schedule around your calendar
- Group size set along with you
- Run as one cohort or split into batches
- Instructor-led (virtual/onsite/hybrid)
- Recurring and annual options
- Trainer sourcing, matching and logistics
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
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- Hands-On Labs on Live Infrastructure, Led by Industry Experts
LLM Inference Optimization and Cost Engineering Course Topics and Program Outline
Six modules follow a clear learning path from inference foundations to a cost-optimized deployment capstone, with hands-on labs on realistic workloads that turn tuning techniques into production-ready skills.
Built by Edstellar with ML inference engineers
Built by ML engineers who optimize LLM inference performance and cost in production, shaped around your stack and use cases.
Baseline each participant's LLM inference optimization readiness before module 01 begins
Build strong LLM inference optimization foundations in instructor-led sessions for your team
Reinforce skills through guided hands-on labs on inference engines and cost tuning
Apply LLM inference optimization skills to real-world scenarios and a capstone deployment project
A short pre-training assessment sets each participant's entry level, so the program starts where your team actually is. The rest is weighted toward hands-on practice: tuning inference engines, quantization tradeoffs, and cost-per-token economics drawn from your team's work, not slides.
Delivered as
Entry level set by the assessment. Depth and schedule customized to your team.
Inference starts with understanding what the serving stack actually does. Work through the decoding pipeline, key-value caching, batching, and request scheduling, and set a performance baseline you will tune against for the rest of the program.
learna)How LLM decoding works: prefill and token generation
b)Key-value caching and attention memory
c)Batching strategies and request scheduling
d)Setting a latency and cost baseline
Measure before you tune. Learn to profile time to first token, tokens per second, and throughput under load, then trace bottlenecks in decoding, networking, or GPU memory so every optimization targets the real constraint.
learna)Time to first token and tokens per second
b)Load testing with realistic request patterns
c)Reading GPU utilization and memory traces
d)Finding the real bottleneck in the pipeline
Cut memory and cost without breaking quality. Practice weight and activation quantization, compare precision formats, and run quality checks that prove the compressed model still answers correctly.
practicea)Precision formats: FP16, INT8, INT4
b)Weight and activation quantization methods
c)Distillation and pruning for smaller models
d)Measuring quality after compression
Configure the engines that serve models in production. Work with continuous batching, paged key-value cache management, and scheduling policies to raise throughput and hold latency steady under real traffic.
practicea)Open serving engines: vLLM, TensorRT-LLM, TGI
b)Continuous batching and request scheduling
c)Paged attention and KV cache management
d)Handling traffic spikes and rate limits
Turn performance into economics. Model GPU costs, compare instance types and spot pricing, right-size deployments for your traffic, and set guardrails that keep cost per million tokens under control as usage grows.
practicea)GPU instance economics and pricing models
b)Cost per million tokens: the metric that matters
c)Right-sizing replicas for real traffic
d)Budgets, alerts, and spend guardrails
Ship it. In the final lab, deploy a tuned, quantized, right-sized serving setup against a realistic production workload, document your latency, cost, and quality tradeoffs, and present the deployment for review.
applya)End-to-end deployment of a tuned serving stack
b)Final latency, throughput, and cost benchmarks
c)Tradeoff review: speed, cost, and quality
d)Capstone presentation and feedback
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
By the end of this program, every participant will be able to tune LLM serving for speed, throughput, and cost. Each outcome is practiced in hands-on labs and shown in the capstone.
Profile inference performance
Measure time to first token, throughput, and cost per million tokens on any serving setup.
Tune decoding and batching
Adjust decoding parameters, batching, and scheduling to cut latency on real workloads.
Apply quantization safely
Compress models to INT8 and INT4 while verifying answer quality stays intact.
Configure serving engines
Set up open serving engines with paged KV cache and continuous batching for production.
Right-size GPU deployments
Match GPU instances and replica counts to real traffic instead of over-provisioning.
Control inference spend
Model GPU economics and set budgets, alerts, and guardrails that keep costs predictable.
Ship a tuned deployment
Combine every technique into a cost-optimized serving deployment with documented tradeoffs.
Teams that Benefit from this LLM Inference Optimization and Cost Engineering Group Training
This program is built for the engineers who ship and operate LLMs, from ML engineers to platform and MLOps teams. Content adapts to each cohort's stack and traffic patterns, so every participant practices the optimizations their role actually owns.
Tune the models your products depend on.
ML engineers master decoding, quantization, and serving configuration, so the models they build run fast and cheap in production.
Workflow friction
- Slow responses hurt the product experience
- Inference costs grow faster than revenue
- Quality drops when models are compressed
How the program helps
- Cut latency on real user workloads
- Shrink cost per million tokens
- Keep quality high after compression
Practice use cases
- Model tuning
- Quantization
- Serving configuration
Run inference infrastructure that scales.
Platform teams learn to configure serving engines, right-size GPU capacity, and set spend guardrails, so inference stays fast, reliable, and affordable.
Workflow friction
- GPU fleets are over-provisioned
- Traffic spikes break latency targets
- Nobody owns the inference cost line
How the program helps
- Right-size GPU capacity to traffic
- Hold latency steady under load
- Own and control inference spend
Practice use cases
- Serving engine setup
- Capacity planning
- Cost monitoring
Make inference economics a decision, not a surprise.
Leaders learn to read latency, cost, and quality tradeoffs, so they fund and sequence LLM investments with clear numbers behind them.
Workflow friction
- Inference bills arrive as surprises
- Hard to judge tuning proposals
- Scaling plans lack cost evidence
How the program helps
- Judge optimization proposals with data
- Plan scaling with cost evidence
- Fund LLM products confidently
Practice use cases
- Architecture reviews
- Scaling decisions
- Budget planning
Each cohort works through role-relevant hands-on labs, then leaves with a tuning playbook for day-to-day serving work.
Prerequisites: Participants should bring working Python skills and familiarity with deploying or calling ML models, plus basic comfort with the command line and cloud concepts. No prior LLM serving experience is needed; the program builds from foundations. Teams that own an LLM application or prototype get the most from the labs, but every exercise runs in the training environment.
ML engineer to production AI leader
The program takes engineers from inference fundamentals to production-grade optimization: decoding and profiling foundations, intermediate quantization and serving-engine work, then advanced cost engineering and a deployment capstone.
Engineers grow from inference basics to owning production serving performance as the modules progress.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Every mode is shaped around your stack before delivery. The curriculum, hands-on labs, and capstone stay the same in each format, so what your engineers can do after training never depends on how you run it.
Hybrid Training
Live instruction paired with supervised hands-on lab practice. Your engineers profile latency, apply quantization, and configure serving engines with instructor guidance throughout. Best for teams shipping or scaling LLM products.
- Live instruction with guided hands-on labs
- Instructors coach every tuning and profiling exercise
- Realistic inference workloads and serving setups
- Best for teams shipping or scaling LLM products
Onsite Training
Your instructor delivers at your facility, with labs set up for your team's stack and models. Engineers tune serving configurations and profile real workloads with direct coaching. Best for co-located teams.
- Instructor delivers at your facility
- Labs set up for your stack and models
- Direct coaching during every exercise
- Best for co-located teams wanting maximum interaction
Live Virtual Training
Live virtual sessions on Zoom or Microsoft Teams, with remote lab access and screen-shared guidance through every tuning task. Your engineers practice profiling and optimization together in real time. Best for distributed teams.
- Live sessions on Zoom or Microsoft Teams
- Remote access to the lab environment
- Screen-shared guidance through every task
- Best for distributed engineering teams
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Frequently Asked Questions
Share your team size, preferred dates, and delivery mode in the lead form. We reply with a tailored proposal covering scope, schedule, and pricing for your group.
Both. Choose live virtual sessions on Zoom or Teams, onsite delivery at your office, or a hybrid mix of instruction and hands-on labs across locations.
The standard program runs 16 hours of instructor-led training with hands-on labs, typically over two days. We can reshape the schedule around your releases.
We recommend 12 to 20 engineers per cohort for strong lab coaching. Larger teams can run as parallel batches.
Yes. We shape the labs, profiling exercises, and capstone around your models, serving stack, and traffic patterns before delivery.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
Inference optimisation follows hands-on use of cloud AI platforms. Once your team serves models through Azure OpenAI, Bedrock, or Vertex AI, it can tune latency and cost per request, manage overall spend with FinOps, and judge when running smaller models on-premise becomes the better option.
Why Choose Edstellar as Your LLM Inference Optimization and Cost Engineering Training Provider
If your team needs to serve LLMs faster and cheaper, start here. Tell us about your models, traffic, and preferred delivery mode, and we will shape a program around them.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content shaped around your LLM inference and cost optimization use cases, so every session maps to your real serving needs.
Proven Methodology
5+ years delivering corporate training to ML and platform engineers across industries and regions.
Practice-Based Learning
6 hands-on labs across 6 modules. Your team builds optimized serving configs and cost models with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your LLM serving stack, with a full trainer profile shared before your program is booked.
Global Scale
One LLM inference program across your offices worldwide, in hybrid, onsite, or live virtual.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your LLM inference capability lift visible.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
LLM Inference Optimization & Cost Engineering Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request LLM Inference Optimization and Cost Engineering Training for Your Team
Share your team size, models and traffic patterns, preferred delivery mode, and timelines. We will respond with a tailored training plan covering scope, schedule, and pricing, and confirm trainer availability.