ARMServe

Inspiration

AI inference in the cloud is becoming increasingly expensive and performance-sensitive. Running a large language model is not simply about choosing a powerful machine—the runtime, model configuration, CPU utilization, memory usage, latency, throughput, and infrastructure cost all interact with each other.

We were particularly interested in Arm64 cloud infrastructure and AWS Graviton because CPU-based inference can provide an efficient alternative for many AI workloads. However, finding the best configuration manually is difficult. Developers often have to experiment with different model and runtime settings, benchmark them, evaluate quality, and compare costs independently.

That led us to our core question:

Can we build a system that automatically discovers better AI inference configurations on Arm64 instead of relying on manual trial and error?

This became the inspiration for ArmServe by TechTronza.

What it does

ArmServe is an AI inference optimization platform designed for Arm64 cloud infrastructure.

Instead of simply running an LLM, ArmServe creates a complete optimization workflow:

AI Workload
     ↓
Baseline
     ↓
Benchmark
     ↓
Experiment
     ↓
Measure
     ↓
Evaluate Quality
     ↓
Analyze Cost
     ↓
Select Better Configuration
     ↓
Deploy
     ↓
Validate

The platform is designed to evaluate multiple dimensions simultaneously:

Latency — how quickly the model responds
Time to first token (TTFT) — responsiveness of LLM inference
Tokens per second — inference speed
Throughput — workload capacity
CPU and memory utilization
Model quality
Infrastructure cost
Configuration efficiency

The goal is not simply to find the fastest configuration.

The goal is to find a configuration that provides the best practical trade-off between performance, quality, and cost.

ArmServe also provides an optimization workflow that can automatically schedule experiments, evaluate their results, and identify promising configurations.

How we built it

We designed ArmServe as a complete cloud AI optimization platform rather than as a single benchmark script.

The architecture separates the user-facing control plane from the actual AI compute workload:

                 ArmServe
                    │
        ┌───────────┴───────────┐
        │                       │
   Web Dashboard            Backend API
        │                       │
        └───────────┬───────────┘
                    │
                    ▼
          Optimization Engine
                    │
          ┌─────────┼─────────┐
          ▼         ▼         ▼
      Benchmark   Quality    Cost
        Engine    Engine    Engine
          │         │         │
          └─────────┼─────────┘
                    ▼
            Optimization Agent
                    │
                    ▼
             Arm64 Workload
                    │
                    ▼
             AWS Graviton
                    │
                    ▼
                 LLM

We built the system around real AI workloads rather than simulated benchmark numbers.

The optimization process starts with a baseline configuration. ArmServe then creates experiments with different configurations, executes them, collects measurements, evaluates the results, and compares them against the baseline.

For a measured metric (M), the relative improvement can be calculated as:

[ Improvement(%) = \frac{M_{baseline}-M_{optimized}} {M_{baseline}}\times100 ]

for metrics where lower is better, such as latency.

For metrics where higher is better, such as throughput:

[ Improvement(%) = \frac{M_{optimized}-M_{baseline}} {M_{baseline}}\times100 ]

The important part is that these values come from actual measurements, rather than predetermined targets.

We also designed the system so that optimization is not allowed to ignore model quality. A configuration that is faster but produces unacceptable quality degradation should not automatically become the winner.

Challenges we ran into

One of the biggest challenges was that AI optimization is not a single-variable problem.

Changing one configuration parameter can affect several other metrics:

Configuration
      │
      ├── Latency
      ├── Throughput
      ├── Memory
      ├── CPU
      ├── Quality
      └── Cost

A configuration that improves throughput may increase memory usage. A configuration that reduces cost may increase latency. A faster configuration may also introduce an unacceptable quality regression.

Another challenge was building a reproducible benchmarking workflow. Performance measurements can vary between runs, so we needed consistent experiment definitions, metric collection, result storage, and comparison logic.

We also had to think carefully about cloud architecture. The API, dashboard, orchestration system, and actual AI workload have different requirements. We therefore separated the control plane from the Arm64 compute workload.

Finally, making the system reliable enough to execute multiple experiments automatically introduced challenges around scheduling, retries, failure handling, resource conflicts, deployment, monitoring, and recovery.

Accomplishments that we're proud of

We are proud that ArmServe is designed as an end-to-end optimization system rather than simply an inference demo.

Our key accomplishments include:

Building an AI optimization workflow around real inference workloads
Targeting Arm64 cloud infrastructure and AWS Graviton
Creating a reproducible benchmarking workflow
Supporting systematic optimization experiments
Tracking latency, throughput, CPU, memory, quality, and cost
Building an optimization engine for comparing configurations
Incorporating quality constraints into optimization decisions
Building experiment scheduling and execution infrastructure
Designing an autonomous optimization workflow
Providing deployment and validation capabilities
Building a dashboard for understanding optimization results
Preparing reproducible evidence for technical evaluation

Most importantly, we focused on building a system that can answer a practical question with evidence:

"Which configuration should we actually deploy, and why?"

What we learned

We learned that AI optimization is fundamentally a measurement and decision-making problem.

It is easy to say that one configuration is faster than another. It is much harder to prove that the improvement is:

Reproducible
Meaningful
Quality-safe
Cost-effective
Deployable
Relevant to the actual target hardware

We also learned that Arm64 optimization requires looking beyond CPU utilization alone. Runtime behavior, model format, threading, memory, workload characteristics, and inference configuration can all influence the final result.

Another important lesson was that optimization should be treated as a continuous loop rather than a one-time benchmark:

Measure
  ↓
Understand
  ↓
Experiment
  ↓
Optimize
  ↓
Validate
  ↓
Deploy
  ↓
Measure Again

That principle became central to ArmServe.

What's next for ARMServe

Our next goal is to continue improving ArmServe as a practical optimization platform for Arm-based AI infrastructure.

We want to expand the optimization space, improve experiment selection, strengthen statistical analysis of benchmark results, and make the autonomous optimization process more efficient.

We also want to make ArmServe easier for developers to reproduce and extend, including better experiment templates, richer benchmark reports, stronger observability, and broader support for Arm-optimized AI runtimes and workloads.

Ultimately, we want ArmServe to become more than a hackathon project: