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The landscape of generative AI is

quickly separating into two distinct yet

complimentary parts. Large language

models, LLM and SLMs, small language

models. So what do those teeth actually

mean and where should we be using them?

That is the purpose of this video today.

Let's get started. While LLMs continue

to push the boundaries of generalized

intelligence and complex reasoning, SLMs

are emerging as the fundamental building

blocks for scalable, efficient,

cost-effective enterprise deployment. A

comprehensive assessment requires us to

move beyond simple size comparisons. We

must evaluate the advanced trade-offs in

architecture, performance dynamics,

operational economics, and deployment

flexibility to build a compliant and

cost-effective AI strategy. The key

message today is that the future of

scalable enterprise AI is inherently

heterogeneous, meaning it will rely on a

mix of both model types. So let's get

started. LLMs or large language models

occupy the upper end of the model scale

defined by massive computational

complexity and broad generalization

capability. This scale is rooted in a

number of parameters which are the

internal variables the model learns

during training. LLMs typically range

from tens of billions of hundreds of

billions of parameters. For example,

frontier models like Llama 3 reportedly

reach 405 billion parameters while GPT 4

is estimated to contain as many as 1

trillion parameters. Now this size

necessitates deep and intricate

transformer designs with many layers

essential for capturing subtle language

patterns and long range contextual

information. They are trained on vast

varied webcale data sets often

encompassing trillions of tokens such as

the entire public internet. Think of an

LLM as a brilliant, highly generalized

consultant who has read every book in

the world and can speak about nearly

every subject with nuance. So what are

SLMs? Small language models. So in

contrast, SLMs are defined by their

compactness and efficiency. Their

parameter counts typically range from a

few million up to approximately 10

billion. Popular examples include

Google's GMA and Microsoft 52 family.

Architecturally, SLMs utilize shallower,

simplified transformer designs with

fewer layers. Critically, the definition

of an SLM is shifting away from being

purely size-based. With the introduction

of highly optimized models,

classification is now increasingly

determined by the model's optimization

for resource constraint deployment and

its capacity for highquality specialized

performance. SLMs leverage smaller, more

curated and domain specific data sets.

The success of models like FI 2 shows

that using highquality, specialized,

textbook quality data yields powerful

reasoning despite a modest size. SLMs

function best as expert specialists in a

narrow field. Now, let's do a technical

deep dive from an architecture and data

regime standpoint. Now, if you look at

your video now, it summarizes the core

architecture difference. On the left,

SLMs have parameters in the millions up

to 10 billion, a shallower design and

rely on curated domain specific data. On

the right, LLM use billions or trillions

of parameters. A deep complex

architecture and massive webcale data.

The significance here is that the SLM's

focus on curated data means that for

specialized enterprise tasks, investing

in data quality yields a higher return

than simply acquiring massive compute

resources. Furthermore, NLM require

specialized centralized GPU clusters

while SLMs are optimized for

decentralized ondevice or mid-tier GPU

deployment which fundamentally impacts

the cost. Now let's look at the

technical deep dive from an operational

economics. The most compelling argument

for SLM lies in their operational

efficiency and reduced TCO or total cost

of ownership. First the inference cost

which is the cost of running the model

in production is dramatically lower.

SLMs can be 10 times less overall and

inference pricing can be reduced by over

100 times per million queries compared

to high-end LLM. Second, inference

latency is better for real-time

applications. SLMs are optimized to

serve tokens in tens of milliseconds,

making them suitable for real world

applications like customer service. LMS

being cloud hosted and often complex,

often incur higher latency measured in

hundreds of milliseconds or more.

Finally, SLMs drastically reduce the

total cost of ownership because they can

run effectively on mid-tier GPUs,

standard CPUs or edge devices, avoiding

the need for massive data center grade

CPU clusters required by LLMs. The lower

opex is like using a fast, cheap local

technician for simple requests instead

of paying for an expensive slow

consulting team to fly in from the

headquarters for every minor query.

Continuing with our technical deep dive,

let's look at optimization and

customization. Now, SLMs maximize

performance within their compact size

through sophisticated optimization

techniques. Knowledge destination or KD

is a primary strategy where a large

teacher LLN transfers its learned

patterns such as complex reasoning paths

to the smaller student SLM. Another

critical technique is quantization which

reduces the numerical precision of model

weights. For example, converting 32-bit

floats to 4bit integers. This

compression can shrink motor size up to

75%. And accelerate inference speeds by

1.56 to 2.4 times. Now, think of

quantization as compressing a large high

resolution photo file into a smaller

JPEG. You retain the necessary quality,

but make it much faster to load.

Furthermore, SLMs are small enough full

fine-tuning on proprietary data,

updating every parameter for extreme

specialization. In contrast, large LLMs

are often too expensive for full

fine-tuning, requiring parameter

efficient finetuning methods. All right,

to continue with our technical deep

type, now let's look at deployment

flexibility. SLMs are the foundational

layer for decentralized AI. Their

compact size makes them uniquely suited

for ondevice or edge deployment running

AI directly on devices like your

smartphones, IoT equipment or autonomous

vehicles. This capability is critical

for achieving data privacy and sovereign

entity. Since SLMs can run locally on

print or entirely offline, regulated

sectors like healthcare or finance can

ensure sensitive data never leaves their

secure environment. This ability to

avoid extreme or external cloud

processing often outweigh the LLM's

superior generalized performance, making

the choice a compliance decision.

However, we must note the robustness

trade-off. While specialized SLMs are

powerful, large LLM still demonstrate

higher resilience against adversarial

partations and immediate reasoning

failures due to their massive scale.

Now, let's talk about use cases. Let's

start with LLM first. LLM are the

generalized expert. They should be

strategically reserved for tasks where

generalized intelligence, high stakes

reasoning, and scale are mandatory. This

includes complex reasoning and

multi-step problem solving that requires

drawing connections across diverse

domains. They are ideal for creative

content generation and complex

sophisticated code generation. NLMs are

also critical for highlevel enterprise

strategy tasks such as large scale risk

analysis, fraud detection and complex

legal synthesis that requires deep

domain bread. Finally, in modern AI

systems, the LLM acts the highle

orchestrator or consultant handling

generalized decision making and

coordinating multi- aent workflows. The

SLM use case is that for the specialized

workhorse. They're ideal for workflows

prioritizing efficiency, accuracy on

narrow tasks, and low latency. This

covers highly specialized tasks like

high accuracy compliance checks in

finance, specialized data parsing or net

Q&A where fine-tuned SLMs can match or

exceed the accuracy of generalized LLMs.

They are essential for real-time

interaction such as high volume customer

support chat bots handling FAQs because

they can respond rapidly in tens of

milliseconds. These small language

models integrate seamlessly into

internal workflows for repetitive

operations like let's say file

annotations or you want to streamline HR

queries. In advanced architecture, the

SLM functions are the highly efficient

worker executing the bulk of operational

subtasks ensuring that the economic

viability of the entire system. Now

let's talk about certain use cases where

you would need hybrid architecture. So

let's talk about agentic AI systems. Now

this is the decade of the AI agents. So

let's start with that. So the adoption

of SLMs is an economic necessity for

scaling advanced agentic AI systems.

Agentic workflows which use AI to

perform sequential tasks can require

dozens or even hundreds of inference

calls or session. If expensive LMS

perform all of these repetitive actions

like structured output, formatting or

simple routing, the cost will scale up

linearly and the system quickly becomes

economically unviable or even

prohibitive for production users. Okay,

what's the solution? It's a hybrid

architecture. We reserve the LLM for

high level orchestration and complex

reasoning and delegate the high

frequency execution tasks to specialized

SLMs. This delegation ensures that the

cumulative cost remains manageable

realizing a significant 10x to 30x cost

advantage in production. So this is like

building a house. The LLM is the

architect creating the plan but the SLMs

are the specialized construction crews.

They are the plumbers. the electricians

doing the repetitive high volume work.

Let's look at another use case which is

the optimizing rack pipelines. So rag

stands for retrieval augmented

generation. Now SLMs also serve as a

strategic cost reducing infrastructure

role in rack pipelines. Now rag is a

process that allows LLM to retrieve and

use external proprietary data by

generating more accurate responses. The

core mechanism relies on generating

embeddings which is numeric AI

representations of data chunks. This is

a high volume highfrequency compute

task. If an LLM is used to generate

these embeddings, the massive volume of

input tokens severely inflates

operational expenses. By utilizing

specialized SLMs to perform this

background work, acting as a

cost-effective research assistant,

enterprises can generate semantic

representations faster and significantly

cheaper. This delegation dramatically

optimizes the total cost of ownership of

the rag infrastructure, transforming a

potentially expensive infrastructural

component into a highly affordable

scalable service. So what is our

conclusion? The evidence overwhelmingly

demonstrates that LLMs and SLMs are

complimentary architectural components.

They are not competitive rivals. The

future of efficient AI is inherently

heterogeneous. We offer four key

strategic recommendations. First,

embrace heterogenity and modularity.

Treat LLMs are the expert consultants

reserved for complex reasoning and SLMs

as a highfrequency operational workh

horses for specialized tasks. Two,

prioritize inference cost optimization.

Actively target SLMs for any routine or

specialized workload because their 10x

to 100x reduction in inference cost is

the single most effective lever for

reducing total operational expenditures.

Third, invest in data quality and

optimization. Shift focus towards

creating highly curated domain specific

training data sets for SLMs and utilize

techniques like robust quantization to

straight models while preserving

performance. And finally, secure data

sovereignty by leveraging the SLM's

capability for ondevice and on-prim

deployment to meet strict compliance

requirements in regulated industries.

And this is true particularly if you're

using working in a heavily regulated

environment like healthcare and pharma

or finance, banking. So with that, we

come to the end of the show. Please

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