EdgeMatrix: Scaling 70B Parameter Models for Enterprise AI

EdgeMatrix: Scaling 70B Parameter Models for Enterprise AI

Scaling to 70B Models on Mid-Range Enterprise Hardware

Using our EdgeMatrix framework, we recently completed benchmarking for the meta-llama/Llama-3.3-70B-Instruct model. The results demonstrate something many considered unthinkable:

Quantized a 142GB FP16 model to a 42.5GB Q4 model
Successfully ran it across mid-range server and workstation-class platforms
Achieved up to 2x faster CPU inference and 50–120% faster GPU inference speeds
Enabled real-world usage of 70B models without relying on hyperscale infrastructure

Key Results Across Platforms

Best achieved speeds:

44.23 tokens/sec on NVIDIA A100
5.65 tokens/sec on CPU (AMD EPYC 9554)

Cost Savings That Make Business Sense

Imagine two enterprises:

Enterprise A uses an A100 GPU without EdgeMatrix and gets around 34.87 tokens/sec. The cost of running this A100 VM is about ~ $3.5/hour.
Enterprise B uses a much more affordable L40S GPU with EdgeMatrix, gets 30.7 tokens/sec, and pays only $0.74/hour.

That means Enterprise B pays ~80% less — while still getting nearly the same performance.

Enterprise B running inference workloads 24/7, this translates to annual savings of approximately ~$24200 per deployment instance ($3.5 – $0.74 × 24 × 365), while maintaining 88% of the performance. For organizations with multiple deployment instances, these savings quickly multiply into hundreds of thousands of dollars annually.

Lets go lil deeper here

Device 1: AMD EPYC 9554 + NVIDIA L40S

Configuration:

CPU: AMD EPYC 9554,
Cores: 60 cores
RAM: 201 GB
GPU: 46 GB (NVIDIA L40S)

Device 2: Intel i7-14700K + NVIDIA RTX 4090

Configuration:

CPU: Intel Core i7-14700K,
Cores: 28 cores
RAM: 94 GB
GPU: 24 GB (NVIDIA RTX 4090)

Device 3: AMD EPYC Processor + NVIDIA A100

Configuration:

CPU: AMD EPYC Processor
Cores: 16 cores
RAM: 105 GB
GPU: 80 GB (NVIDIA A100)

Device 4: AMD EPYC 9554 (Pure CPU)

Configuration:

CPU: AMD EPYC 9554
Cores: 32 cores
RAM: 117 GB

Device Comparison Summary

We benchmarked Llama-3.3-70B across multiple CPU-GPU configurations — both with and without EdgeMatrix — to measure the real-world impact on inference performance.

Precision Retained: Raw vs Quantized Prompt Evaluation

To validate that performance gains did not come at the cost of intelligence or reliability, we compared the raw Llama-3.3-70B-Instruct model (142GB FP16) with the EdgeMatrix-optimized quantized version (Q4_K, 42.5GB) across reasoning and language comprehension prompts.

Prompt Topics Included:

Arithmetic logic
Pattern recognition (Fibonacci series)
Algebraic reasoning
Propositional logic
Time-distance problem solving

What We Observed:

Despite being compressed to less than 1/3rd the original size, the quantized model using EdgeMatrix:

Preserved the chain-of-thought reasoning
Delivered identical or near-identical explanations
Maintained step-by-step math and logic flow
Retained final answers and interpretability

EdgeMatrix didn’t just preserve output — it preserved process.

This confirms that compression + optimization can go hand-in-hand with quality, making large model deployment feasible without compromising the intelligence that enterprises need.

Prompts we tested goes here….

Prompt 1:

Question: The sum of the ages of three individuals—A, B, and C—is 90 years. What was the total of their ages four years ago?

Options:

A) 74 years
B) 78 years
C) 86 years
D) 80 years

Answer: B) 78 years

Raw Model Response

To find the total of their ages four years ago, we need to subtract 4 years from each of their current ages and then sum those values. Since there are three individuals, we subtract a total of 4 * 3 = 12 years from their current total age.
Current total age = 90 years
Total age four years ago = Current total age - 12 years
Total age four years ago = 90 - 12
Total age four years ago = 78 years
The correct option is B) 78 years.

Quantized Model Response:

Quantized Model Response:
To find their total age four years ago:
•	There are 3 individuals.
•	Each was 4 years younger, so total reduction = 3 × 4 = 12 years.
•	Current total age = 90 years
 → Total age four years ago = 90 − 12 = 78 years
Answer: B) 78 years

Prompt 2:

Question: Identify the number that disrupts the Fibonacci sequence in the series: 1, 1, 2, 3, 6, 8, 13, 21, …

Options:

A) 13
B) 8
C) 21
D) 6

Answer: D) 6

Raw Model Response

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding numbers, starting from 1 and 1. 
The correct Fibonacci sequence is: 1, 1, 2, 3, 5, 8, 13, 21, ...
In the given series, the number that disrupts the sequence is 6, which should be 5 according to the Fibonacci sequence.
The correct option is D) 6.

Quantized Model Response:

The Fibonacci sequence begins: 1, 1, 2, 3, 5, 8, 13, 21, ...
Each term is the sum of the two preceding numbers.
In the given series, 6 appears in place of 5, which breaks the pattern.
The number that disrupts the sequence is: 6

Prompt 3:

Question: The ratio of two numbers, a and b, is 3:7. After adding 9 to each number, the ratio becomes 9:17. What are the original numbers a and b?

Options:

A) 8 and 42
B) 9 and 21
C) 15 and 35
D) 18 and 42

Answer: D) 18 and 42

Raw Model Response

Given that the ratio of a and b is 3:7, we can express a and b as 3x and 7x, respectively, where x is a constant.
The ratio of (a + 9) and (b + 9) is 9:17, so we can set up the equation:
(a + 9) / (b + 9) = 9/17
Substituting a = 3x and b = 7x, we get:
(3x + 9) / (7x + 9) = 9/17
Cross-multiplying, we get:
17(3x + 9) = 9(7x + 9)
Expanding, we get:
51x + 153 = 63x + 81
Subtracting 51x from both sides, we get:
153 = 12x + 81
Subtracting 81 from both sides, we get:
72 = 12x
Dividing both sides by 12, we get:x = 6
 Now that we have the value of x, we can find the original numbers a and b:
a = 3x = 3(6) = 18
b = 7x = 7(6) = 42
 The correct option is D) 18 and 42.

Quantized Model Response:

Given that the ratio of a and b is 3:7, we can express a and b as 3x and 7x, respectively, where x is a constant.
The ratio of (a + 9) and (b + 9) is 9:17, so we can set up the equation:
(a + 9) / (b + 9) = 9/17
Substituting a = 3x and b = 7x, we get:
(3x + 9) / (7x + 9) = 9/17
Cross-multiplying, we get:
17(3x + 9) = 9(7x + 9)
Expanding, we get:
51x + 153 = 63x + 81
Subtracting 51x from both sides, we get:
153 = 12x + 81
Subtracting 81 from both sides, we get:
72 = 12x
Dividing both sides by 12, we get:x = 6
 Now that we have the value of x, we can find the original numbers a and b:
a = 3x = 3(6) = 18
b = 7x = 7(6) = 42
 So the answer is 18 and 42

Prompt 4:

Question: What is the inverse of the proposition: “If a triangle is equiangular, then it is equilateral”?

Options:

A) If a triangle is not equilateral, then it is not equiangular.
B) If a triangle is not equiangular, then it is not equilateral.
C) If a triangle is equilateral, then it is equiangular.
D) If a triangle is equilateral, then it is not equiangular.

Answer: B) If a triangle is not equiangular, then it is not equilateral.

Raw Model Response

The given proposition is: "If a triangle is equiangular, then it is equilateral."
To find the inverse, we need to negate both the hypothesis and the conclusion, and then swap them.
Original proposition: If P (equiangular), then Q (equilateral)
Inverse proposition: If not Q (not equilateral), then not P (not equiangular)
So, the inverse of the proposition is: "If a triangle is not equilateral, then it is not equiangular."
The correct option is A) If a triangle is not equilateral, then it is not equiangular.

Quantized Model Response:

Given:
Proposition:
 "If a triangle is equiangular, then it is equilateral."

Step: Inverse
To form the inverse, negate both the hypothesis and conclusion:
•	Original:
  If P (equiangular), then Q (equilateral)
•	Inverse:
  If not Q (not equilateral), then not P (not equiangular)

Final Statement:
Inverse:
 "If a triangle is not equilateral, then it is not equiangular."

Prompt 5:

Question: A train departs from Agra at 5:00 AM and arrives in Delhi at 9:00 AM. Another train leaves Delhi at 7:00 AM and reaches Agra at 10:30 AM. At what time do the two trains cross each other?

Options:

A) 6:36 AM
B) 6:56 AM
C) 7:00 AM
D) 7:56 AM

Answer: D) 7:56 AM

Raw Model Response

To find the time at which the two trains cross each other, we need to find the time at which they meet.
Let's assume the distance between Agra and Delhi is D. 
The first train travels from Agra to Delhi in 4 hours (from 5:00 AM to 9:00 AM). So, its speed is D/4.
The second train travels from Delhi to Agra in 3.5 hours (from 7:00 AM to 10:30 AM). So, its speed is D/3.5.
 When the two trains meet, the sum of the distances they have traveled is equal to the total distance D.
 
Let's say they meet after x hours from 5:00 AM. Then, the first train has traveled for x hours, and the second train has traveled for (x - 2) hours (since it started 2 hours after the first train).
The distance traveled by the first train is (D/4)x, and the distance traveled by the second train is (D/3.5)(x - 2).
Since they meet at this point, the sum of their distances is equal to D:
(D/4)x + (D/3.5)(x - 2) = D
To simplify the calculation, let's multiply both sides by 14 (the least common multiple of 4 and 3.5):
3.5x + 4(x - 2) = 14
Expanding and simplifying:
3.5x + 4x - 8 = 14
Combine like terms:
7.5x = 22
Divide by 7.5:
x = 22/7.5
x = 2.93 hours (approximately)
Since they meet after 2.93 hours from 5:00 AM, we can convert this to minutes:
2 hours 56 minutes ≈ 2.93 hours
Adding this to 5:00 AM, we get:
5:00 AM + 2 hours 56 minutes = 7:56 AM
The correct option is D) 7:56 AM.

Quantized Model Response:

Given:
Train A (Agra → Delhi):
  Departs at 5:00 AM, arrives at 9:00 AM → Time = 4 hours, speed = 𝐷⁄4
Train B (Delhi → Agra):
  Departs at 7:00 AM, arrives at 10:30 AM → Time = 3.5 hours, speed = 𝐷⁄3.5

When the two trains meet, the total distance covered by both is equal to the total distance D.
Let x be the time (in hours) after 5:00 AM when they meet.
 Then:
•	Train A travels for x hours → distance = (𝐷⁄4)·x
•	Train B starts 2 hours later → travels for (x − 2) hours → distance = (𝐷⁄3.5)·(x − 2)
So:
(𝐷⁄4)·x + (𝐷⁄3.5)·(x − 2) = 𝐷
To eliminate D, divide both sides by D:
(x⁄4) + ((x − 2)⁄3.5) = 1
Multiply entire equation by 14 (LCM of 4 and 3.5):
3.5x + 4(x − 2) = 14
 3.5x + 4x − 8 = 14
 7.5x = 22
 x = 22⁄7.5 = 2.93 hours
Convert 0.93 hours to minutes:
 0.93 × 60 = 56 minutes
So, time of meeting = 5:00 AM + 2 hours 56 minutes = 7:56 AM

So the answer is 7.56 AM

Why This Matters

Traditionally, 70B parameter models were confined to specialized hyperscale environments. EdgeMatrix is challenging that notion by:

Reducing memory footprint by over 70% through advanced quantization
Accelerating inference across accessible enterprise hardware
Maximizing compute efficiency for real-world deployments
Lowering the entry barrier for enterprises to adopt large LLMs affordably

This is a critical enabler for enterprises who want AI at scale without depending solely on hyperscalers.

The Road Ahead

EdgeMatrix’s vision continues to evolve.

We are actively working on:

Dynamic runtime acceleration methods to adapt models to a wider range of enterprise and edge devices.
Seamless AI integration layers to bring large language models closer to real-world enterprise workflows without infrastructure disruption.
EdgeMatrix Extensions: Expanding hardware support, including next-generation AI accelerators.

When we launched EdgeMatrix, our goal was clear: To accelerate the adoption AI across edge devices, after success with CNNs and other models in the past, now we have taken up on challenge to bring large language models (LLMs) across edge devices and enterprise applications — while dramatically reducing AI deployment costs.

With our latest 70B model benchmarks, we’re proving that even the largest models can be optimized for practical, cost-efficient deployment, without the need for hyperscale infrastructure.

The goal remains simple but bold: Make enterprise-grade LLMs truly accessible anywhere – faster, smarter, and leaner.

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