What if you skipped PyTorch entirely? What if you wrote your own matrix math, talked directly to the hardware, and trained a language model in a compiled systems language? That sounds extreme. But developers who care about understanding what is actually happening under the hood are doing exactly this in 2026.

This is not a guide for production machine learning teams. It is for the curious developer who wants to train an LLM on Apple Silicon the hard way, and learn something real in the process. We will look at the tools, approaches, and tradeoffs involved in doing low-level ML work outside the Python ecosystem.

Why Anyone Would Avoid Python for ML Work

Python is the default language for machine learning. That is not an accident. Libraries like PyTorch and TensorFlow have years of optimized kernel work behind them. They are fast, well-documented, and widely supported.

But Python itself never runs your matrix math. It orchestrates C++ and CUDA code under the hood. For many developers, that abstraction is a black box. You call matmul() and something fast happens. You have no idea what.

For developers who want to understand what a forward pass (the step where inputs flow through a neural network to produce a prediction) actually costs in memory and compute cycles, the Python layer hides too much. Writing your own kernels in Swift, Rust, or C forces you to confront every byte of data movement. That is uncomfortable. It is also genuinely educational in a way that calling library functions never is.

There is also a practical angle. Apple Silicon chips have a unique hardware layout. The CPU, GPU, and a dedicated matrix coprocessor called the AMX (Apple Matrix Coprocessor) all share the same unified memory pool. This architecture rewards code that understands the hardware. Generic Python wrappers do not always take full advantage of it. When you are debugging your model outputs, tools like a digital footprint audit tool remind you how much invisible infrastructure surrounds even a simple compute workload.

The Four Compute Units on Apple Silicon

Before writing a single line of code, it helps to understand what you are working with. Apple Silicon gives you four distinct compute targets, each with very different performance profiles for matrix math.

CPU scalar: The baseline. One multiplication at a time. Useful for correctness testing, not speed.
SIMD (Single Instruction, Multiple Data): The CPU can process multiple floating-point values in one instruction using vector registers. On Apple Silicon, SIMD units can handle 4 to 8 floats simultaneously. This can multiply throughput several times over scalar code.
AMX: Apple's matrix coprocessor. It sits alongside the CPU cores and is designed specifically for matrix operations. It is not directly programmable through public APIs, but calling Apple's Accelerate framework exposes it indirectly. Some developers use undocumented AMX instructions at their own risk.
GPU via Metal: The GPU has hundreds of shader cores and can run thousands of threads in parallel. For large matrix multiplications, this is where the biggest performance gains live.

Each step up this ladder can add an order of magnitude in throughput. Going from naive scalar C to a well-tuned Metal shader is the difference between gigaflops and teraflops. That gap matters enormously when you are running thousands of training iterations.

Understanding how data is encoded at each stage also matters. Numbers in memory are just bits, and knowing how binary representations work helps when debugging precision issues in floating-point kernels. Our complete binary translation guide is a useful reference for that foundational layer.

Starting Point: A Minimal GPT-Style Model in Swift

The reference model that low-level ML hobbyists often use is a GPT-2-compatible architecture. Andrej Karpathy's llm.c project on GitHub is roughly 1000 lines of plain C and covers everything: embeddings, attention, feedforward layers, and backpropagation. It is a realistic workload, not a toy benchmark.

Porting this to Swift is a useful exercise. Swift compiles to native code via LLVM. It has direct access to SIMD intrinsics through the simd module. It can call Metal shaders through the MetalKit framework. And it can call into C libraries via bridging headers if you need to drop down even further.

Benefits of Low-Level ML Development

✓

Understand Hardware
Gain insight into memory and compute cycles.

✓

Custom Kernels
Write your own matrix math in Swift.

✓

Performance Optimization
Utilize Apple Silicon's unique architecture.

✓

Educational Experience
Learn the intricacies of ML beyond abstractions.

The core operation that dominates training time is matrix multiplication. In a GPT-style model, attention and feedforward layers are almost entirely matmuls. Getting this one operation fast unlocks most of the training throughput you care about.

Here is what a naive Swift matrix multiply looks like before any optimization:

func matmulNaive(
  _ A: [Float], _ B: [Float], _ C: inout [Float],
  M: Int, N: Int, K: Int
) {
  for i in 0..



This is correct. It is also extremely slow. On an M-series chip, this might run at a few gigaflops. The same hardware can theoretically deliver several teraflops for matrix math. The gap between those two numbers is the entire optimization journey.



Three Optimization Steps That Actually Move the Needle

Step 1: Cache-Friendly Memory Access

The naive triple-loop above has a memory access problem. The inner loop walks down column j of matrix B. In row-major storage, that means jumping by N elements between each access. The CPU cache hates this. You are constantly missing cache lines and waiting for memory.



Transposing matrix B before the multiply turns column accesses into row accesses. Both matrices now stream sequentially through memory. Cache hit rates improve dramatically. This single change can double or triple throughput before touching any SIMD code.

func transpose(_ M: [Float], rows: Int, cols: Int) -> [Float] {
  var T = [Float](repeating: 0, count: rows * cols)
  for r in 0..


Step 2: SIMD Vectorization

Swift's simd module exposes four-wide and eight-wide float vectors directly. Instead of multiplying one float at a time, you multiply four at once. The compiler can sometimes do this automatically with the right optimization flags. Writing it explicitly ensures it happens.

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import simd

func dotProductSIMD(_ a: UnsafePointer, _ b: UnsafePointer, count: Int) -> Float {
  var acc = SIMD4.zero
  let chunks = count / 4
  for i in 0..(a[i*4], a[i*4+1], a[i*4+2], a[i*4+3])
    let vb = SIMD4(b[i*4], b[i*4+1], b[i*4+2], b[i*4+3])
    acc += va * vb
  }
  return acc.x + acc.y + acc.z + acc.w
}


Step 3: Parallelism with Swift Concurrency

Modern Apple Silicon chips have multiple performance cores. A single-threaded matmul uses only one of them. Swift's structured concurrency (using async and TaskGroup) makes it straightforward to divide the output rows across threads.

The key is partitioning work so threads do not share write targets. Each thread writes to a different slice of the output matrix. No locks, no contention. This can scale linearly with core count up to the point where memory bandwidth becomes the bottleneck.

When to Reach for Metal

CPU optimizations have a ceiling. Even with SIMD and multi-threading, the CPU cannot compete with the GPU for large matrix sizes. The GPU's advantage is raw parallelism: hundreds of shader cores all doing math at the same time.

Metal is Apple's GPU programming framework. Writing a matrix multiply kernel in Metal Shading Language (which looks a lot like C++) and dispatching it from Swift is not as hard as it sounds. The tricky part is memory layout. GPU threads work in groups called threadgroups, and getting your data arranged so threads in the same group access nearby memory is critical for performance.

A simple Metal matmul kernel looks like this:

kernel void matmul(
  device const float* A [[buffer(0)]],
  device const float* B [[buffer(1)]],
  device float* C       [[buffer(2)]],
  constant uint& N      [[buffer(3)]],
  constant uint& K      [[buffer(4)]],
  uint2 gid             [[thread_position_in_grid]])
{
  uint row = gid.y;
  uint col = gid.x;
  float sum = 0.0;
  for (uint k = 0; k < K; k++) {
    sum += A[row * K + k] * B[k * N + col];
  }
  C[row * N + col] = sum;
}


This naive Metal kernel is already faster than optimized CPU code for large matrices. Advanced techniques like tiled shared memory (loading chunks into threadgroup memory before computing) push performance much further. Check the Unreal Engine GPU optimization docs for real-world examples of tiled memory patterns that translate well to Metal.

When you are ready to expose your trained model through an API endpoint, the Postman API platform is a solid choice for testing and documenting those endpoints before sharing them with your team.

Alternative Approaches Worth Knowing

Swift is not the only non-Python path. Developers exploring this space in 2026 have a few other serious options.

Rust has become a popular choice for ML infrastructure work. The Candle framework from Hugging Face is a minimalist ML framework written entirely in Rust. It supports Metal backends on Apple Silicon. The safety guarantees of Rust's borrow checker are genuinely useful when managing GPU buffers manually.

Zig is another option that some developers prefer for its simplicity and direct C interoperability. Zig has no hidden control flow, which makes it easier to reason about what the CPU is actually doing. It does not have mature ML tooling yet, but for writing raw compute kernels it is worth considering.

Plain C with Accelerate is the pragmatic middle ground. Apple's Accelerate framework exposes BLAS (Basic Linear Algebra Subprograms) routines that hit the AMX coprocessor. Calling cblas_sgemm from C or Swift gives you near-peak matrix multiply performance without writing a single kernel. It is not as educational, but it is extremely fast.

For teams building real products rather than learning exercises, the OpenAI research team's published work consistently shows that hardware-aware kernel optimization matters as much as algorithmic improvements at scale. If you are building a web interface around your model and need a GraphQL API layer, the GraphQL learning guide is a well-structured starting point for that side of the stack.

Measuring What You Actually Built

Raw throughput numbers in gigaflops or teraflops are satisfying but incomplete. What matters for LLM training is wall-clock time per training iteration. A full forward and backward pass through a GPT-2-sized model involves dozens of matrix multiplications plus attention, softmax, layer normalization, and gradient accumulation.

Building a proper test harness matters. You want to measure the whole training loop, not just the matmul in isolation. A microbenchmark that shows impressive GFLOP/s numbers can still produce a slow training loop if memory transfers between CPU and GPU are not optimized.


  Python vs. Low-Level ML Languages
  
    
      Python Libraries
      Fast, well-documented, abstracted
    
    
      Swift Development
      Direct hardware access, educational
    
    
      C/C++ Kernels
      Performance-focused, requires deep understanding
    
    
      Rust Implementation
      Memory-safe, high-performance, complex
    



Useful metrics to track per iteration:


  Wall-clock time in milliseconds
  Peak memory usage in megabytes
  Effective GFLOP/s across the full iteration
  GPU utilization percentage (via Instruments or Metal System Trace)


Apple's Instruments tool, bundled with Xcode, gives you a Metal System Trace that shows exactly where your GPU time is going. It is worth learning. Many developers are surprised to find their GPU is idle for large fractions of the training loop because the CPU cannot feed it data fast enough.

If you are exploring AI-assisted tools for debugging or reviewing your code logic during this process, the TalkBuoy AI assistant can help you think through optimization strategies in real time, including debate mode where you can argue your architectural choices against an AI counterpoint.

Useful Tools for the Non-Python ML Developer

Building LLM training infrastructure outside Python means assembling your own toolchain. Here are tools that developers in this space actually use.




  Xcode Instruments: Essential for profiling Metal shaders and finding CPU bottlenecks.
  Accelerate framework: Apple's BLAS and vDSP routines. Free, fast, and already on your Mac.
  Metal Performance Shaders (MPS): Pre-built GPU kernels for common operations including matrix multiply. Useful as a reference implementation to benchmark against.
  llm.c on GitHub: The plain C reference implementation that many developers port to other languages. Clean, readable, and realistic.


Model outputs often arrive as raw JSON payloads. Parsing and inspecting those payloads during development is much easier with a dedicated tool. A good JSON formatter saves real time when you are staring at deeply nested inference responses and trying to spot where a value went wrong.

When you are building a web dashboard to visualize training metrics, consistent color choices matter more than you might expect. Our color palette generator can help you quickly pull a coherent set of colors for your loss curves, attention maps, and metric charts without spending an hour in a design tool.

If your project involves sharing model outputs or demos with others, a free QR code generator can help you quickly create scannable links to hosted inference endpoints or demo pages. It takes about ten seconds and saves you from typing long URLs at a presentation.

For developers building Python-based API backends to serve their Swift-trained models, the Django documentation is a reliable reference for structuring REST endpoints cleanly.



Is This Worth Doing in 2026?

Honestly, it depends on your goal. If you need to train a production model, use Apple's MLX framework or PyTorch with MPS backend. They have years of optimization work behind them and will outperform handwritten code for almost every workload.

But if your goal is to understand what a transformer actually does to your hardware, writing it yourself is irreplaceable. You will learn things about memory bandwidth, cache behavior, and parallel execution that no tutorial or course will teach you. That knowledge makes you a better engineer even when you go back to using frameworks.

The growing interest in systems-level ML work also has practical implications. As models get deployed on edge devices and custom hardware, the engineers who understand what happens below the Python layer become increasingly valuable. Knowing how to squeeze performance out of a specific chip architecture is a skill with a long shelf life.

Want to go deeper? Check out the Buffer Resources blog for case studies on how technical teams document and share engineering knowledge. That is useful when you are writing up what you learned from this kind of exploration. And when you eventually publish your project publicly, run a quick digital footprint audit to make sure you have not accidentally exposed API keys or personal data in your commits or public profile.

Where to Go From Here

The path from a naive triple-loop matmul to a Metal-accelerated training loop is steep but well-defined. Start with correctness. Get the numbers right before chasing speed. Then profile before optimizing. Do not guess where your bottleneck is.

The order of attack that tends to work well is: fix memory layout first, add SIMD second, add threading third, and move to Metal last. Each step compounds on the previous one. Skipping ahead to Metal before fixing memory access patterns often produces disappointing results.

For developers who want a capable machine for this kind of work, the High-End Gaming PC Iceflow RGB with RTX 4070 is worth considering if you want a Windows machine with CUDA support alongside your Mac setup. CUDA still has a significant ecosystem advantage for GPU kernel development, and having both platforms lets you compare approaches. This post contains affiliate links. If you make a purchase through these links, we may earn a commission at no additional cost to you.

The community around low-level ML on Apple Silicon is small but active. If you build something interesting, write it up. The best way to learn is to explain what you did to someone else, and the best way to improve is to have someone tell you what you got wrong. Try building your own minimal matmul, benchmark it against Apple's Accelerate BLAS, and see how close you can get.

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