.NET a Memory Hog and My Journey to Nim

Introduction

When I began auditing our server performance, I noticed a concerning trend with .NET. A minimal API server, built with basic endpoints and no additional services, consumed 40 MB of RAM at startup. This seemed reasonable until I compared it to a Node.js server, heavily laden with libraries and boilerplate code, which also idled at 40 MB.

The Unexpected Memory Growth

My initial assumption was that .NET’s footprint was a startup artifact — a temporary overhead that would stabilize. However, under routine API requests — just basic database queries with no significant traffic — memory usage climbed to 200 MB, occasionally peaking at 500 MB. Even after requests completed, memory was not released. The garbage collector.

The “Ratchet Effect” Under Load

This persistent memory retention became a critical issue during burst scenarios. When the server faced high request volumes, memory usage spiked dramatically and never returned to baseline, creating a ratchet effect that strained system resources. The impact extended beyond the .NET process itself. Other services, such as a co-located PostgreSQL instance, suffered degraded performance — slower queries, delayed writes — because .NET was monopolizing available RAM.

Scaling Limitations

Scaling the application was equally problematic. Each additional .NET process required a minimum of 100 MB, making it impractical to run multiple processes for cron jobs or background workers. Consolidating logic into a single process was the only viable option — but this introduced a single point of failure: if the process crashed, the entire application went down.

Attempts to Mitigate the Problem

Frustrated, I explored workarounds like Docker memory caps and environment variables to limit heap usage. I also experimented with garbage collector tweaks, hoping concurrent mode would alleviate the issue. While these adjustments offered marginal improvements, they didn’t address the core problem: .NET’s memory management required constant oversight. As a developer, I didn’t want to spend time managing system resources; the technology should handle that seamlessly.

The Need for a Leaner Alternative

Why was I fighting the framework to make it perform efficiently? The tools and runtime should optimize memory usage out of the box, allowing me to focus on writing application logic, not fine-tuning performance knobs. This realization pushed me to explore alternative languages that delivered high performance with minimal memory overhead and less developer friction.

Evaluating Alternatives: Rust, Go, and Nim

1. Rust — Powerful but Demanding

I was immediately impressed by Rust’s syntax and features — its type system, enums, and optional types were outstanding. Rust’s focus on memory safety and zero-cost abstractions made it a strong contender. However, for daily server tasks like writing basic APIs, Rust felt cumbersome. The borrow checker introduced significant mental overhead for simple CRUD operations. I recognized Rust’s power for systems or IoT programming, but it wasn’t the right fit for fast-paced backend development.

2. Go — Simple, Solid, but Not My Style

Next, I turned to Go. Its simplicity and performance were undeniably impressive: a basic Go server consumed only 20–40 MB under load. The garbage collector was efficient, and performance was stable. But — don’t get me wrong — I respect Go as a language. It’s fast, predictable, and does its job well. It just didn’t suit my style. Go felt overly simple, with a type system that lacked the richness I valued. The absence of traditional object-oriented programming (OOP) and its repetitive if err != nil error handling made the development experience feel a bit too minimalistic for my taste. In short, Go is great for what it is — but I was looking for a language that offered more expressiveness and depth without losing performance.

3. Nim — The Balanced Contender

Finally, I discovered Nim, and it was a revelation. Nim combined the best traits of Rust, Go, and Python while addressing the shortcomings I found elsewhere. Its Python-like syntax made it approachable, and its feature set — optional types, generics, async/await, templates, and macros — gave it the flexibility I wanted.

Why Nim Won Me Over

Simplicity Meets Performance

Nim introduced a groundbreaking memory model called Optimized Reference Counting (ORC). Unlike .NET’s mark-and-sweep GC, ORC doesn’t rely on traditional global garbage collection. Instead, it performs compile-time insertion of destructors, optimizing out 80–90% of reference counting code entirely and fallbacks to Automatic Reference Counting for 10%-202% remaining cases. This means that in the vast majority of cases, memory is freed immediately and deterministically, matching Rust’s performance while retaining the simplicity of a managed language.

How ORC Works

The ORC system is not the typical “stop-the-world” garbage collector found in managed runtimes. It’s a lightweight, local-context-scoped, purely atomic, and thread-safe system — no global locks involved. ORC triggers only for cycle collection, and even then, it operates at an optimized threshold, scanning only objects that are most likely cycles instead of sweeping the entire heap. This approach results in minimal CPU overhead with no pauses at all, making Nim ideal for high-throughput, low-latency servers.

Real-World Results

A barebones Nim server started at just 3–4 MB of RAM, an order of magnitude smaller than .NET’s 40 MB. Under normal load, memory usage rarely exceeded 20 MB, even during burst scenarios — almost exactly matching the real memory required by the workload. Nim reclaimed memory efficiently and predictably, allowing me to run multiple lightweight processes — API servers, background jobs, and cron tasks — on the same machine without affecting PostgreSQL or other services.

Developer Experience

Nim aligned beautifully with my OOP mindset and included try-catch exception handling similar to .NET, eliminating the verbosity I disliked in Go. Its optional types, generics, and high-level abstractions provided the safety of Rust and the readability of Python — all with a fraction of the complexity. It felt like the language was working with me, not against me.

Conclusion: Nim as the Successor to .NET in My Workflow

In the end, Nim proved to be the successor I was searching for. It combined Rust’s performance, Python’s readability, and .NET’s developer-friendly abstractions — without the memory bloat. With Nim, I could write less code, focus on application logic, and trust the language to handle memory efficiently and deterministically.

For developers frustrated with .NET’s memory footprint or looking for a lightweight, high-performance alternative, Nim is a compelling choice. It’s not just a programming language — it’s a developer’s ally that lets you stop fighting technology and start building.