Elixir bindings and Nx backend for Apple's MLX.

Emily runs Nx computations on Apple Silicon through MLX. Installing it as the default Nx backend is enough to get Bumblebee models executing on the Metal GPU with no further integration work — DistilBERT, Qwen3, ViT, and Whisper all run against pinned reference outputs in the conformance suite today.

The library is structured as four thin layers, each independently testable against its own oracle:

Emily.Compiler    (Nx.Defn.Compiler) — validates opts, pins the result backend
Emily.Backend     (Nx.Backend)       — op-by-op translation to Native
Emily.Native      (NIF shim)         — one function per MLX op, no policy
MLX C++                              — statically linked into libemily; mlx.metallib alongside

Add :emily to your mix.exs deps:

def deps do
  [
    {:emily, "~> 0.3"}
  ]
end

On first mix compile Emily downloads the precompiled NIF for your OS/arch/variant (libemily.{so,dylib} + mlx.metallib) from this repo's GitHub releases into $EMILY_CACHE (default $(getconf DARWIN_USER_CACHE_DIR)emily/ on macOS, ${XDG_CACHE_HOME:-~/.cache}/emily/ on Linux) and drops it into priv/. No cmake, Xcode, or C++ toolchain is required on the consumer side — nothing is compiled locally. See Building for details.

• Nx backend. Every Nx.* op dispatches to MLX; ops without a native primitive fall back transparently to Nx.BinaryBackend with a [:emily, :fallback, *] telemetry event. See the Fallbacks section of Emily.Backend for the per-op catalogue.
• Defn compiler. Emily.Compiler runs defn / Nx.Serving / Bumblebee inference on MLX. Backs the results with lazy MLX graphs.
• Fused transformer kernels. Emily.Fast exposes mx::fast::rms_norm, layer_norm, rope, and scaled-dot-product attention as defn-callable helpers with composed-defn fallbacks for other backends.
• Affine group-wise quantization. Emily.QuantizedWeight + Emily.Quantization wrap MLX quantize / dequantize / quantized_matmul for int2 / int4 / int8 inference. Includes a defn-native dequantize_defn/1 for quantized layers inside Axon forward passes.
• Mixed-precision training. Emily.MixedPrecision provides the bf16 recipe (cast params for the forward, keep f32 master, dynamic loss scaling with overflow detection).
• Per-process Metal streams. Emily.Stream lets each BEAM process own its own Metal command queue, so multiple processes can share a model and run inference concurrently.
• Zero-copy to_binary. Nx.to_binary/1 on an Emily tensor returns a BEAM resource binary aliasing the MLX buffer — no memcpy.
• Telemetry. [:emily, :eval, *], [:emily, :to_binary, *], [:emily, :fallback, *], and [:emily, :memory, :stats] span events. See Emily.Telemetry.
• Compile-time debug flags. :debug_bounds_check and :debug_detect_nan_inf re-enable runtime assertions on hot paths that GPU backends skip by default. Both default off with zero runtime cost.

• macOS on Apple Silicon (arm64). Emily's precompiled NIFs are arm64 macOS only; x86_64 Macs aren't supported.
• Elixir 1.18+ / OTP 27+. Development is pinned to Elixir 1.19.5 / OTP 28.3 via .tool-versions.

No Xcode, Metal toolchain, cmake, or C++ compiler is required on the consumer side — Emily downloads a precompiled NIF from GitHub Releases on first mix compile.

Add {:emily, "~> 0.3"} to mix.exs, then:

mix deps.get
mix compile

On a cold build Emily downloads the matching precompiled tarball (emily-nif-<version>-<variant>-<target>.tar.gz) from this repo's GitHub release for the pinned version, verifies its SHA256 against the .sha256 sidecar fetched alongside it (no checksums baked into mix.exs — the sidecar is the source of truth for the published asset), and extracts libemily.{so,dylib} + mlx.metallib into priv/. Subsequent builds reuse the cached tarball under $EMILY_CACHE (default $(getconf DARWIN_USER_CACHE_DIR)emily/ on macOS, ${XDG_CACHE_HOME:-~/.cache}/emily/ on Linux). The sidecar is re-fetched on every compile and the cached tarball is re-verified against it, so a republish with a new checksum invalidates a stale cache automatically.

Override the cache location with EMILY_CACHE=/some/path mix compile.

git clone https://github.com/ausimian/emily.git
cd emily
mix deps.get    # also clones ml-explore/mlx into deps/mlx_src at the pinned tag
mix compile

The in-repo checkout keeps c_src/ on disk, so mix compile takes the source-build path: scripts/build-mlx.sh cmake-builds libmlx.a + mlx.metallib out of deps/mlx_src/ into $EMILY_CACHE/mlx-<version>-<variant>/, then elixir_make links the NIF against it. Xcode + the Metal toolchain are required.

Force an MLX rebuild with mix compile.emily_mlx --force after editing scripts/build-mlx.sh or bumping @mlx_version.

MLX can ship its Metal kernels either AOT-compiled into mlx.metallib, or as source strings that are JIT-compiled on first use. Emily defaults to the AOT variant. To switch, add to config/config.exs:

config :emily, variant: :jit

This changes which prebuilt tarball is downloaded; both variants can coexist under $EMILY_CACHE.

Artefact sizes on an M-series Mac (release optimisations):

With JIT on, kernels are compiled on first invocation, so there's a small per-kernel warm-up cost at runtime; subsequent calls are cached in-process. All of Emily's test suite passes under both variants.

The JIT prebuilt is built against the macOS 26.2+ SDK (MLX's NAX kernel sources transitively include <MetalPerformancePrimitives/MetalPerformancePrimitives.h>, which only ships in that SDK) and references half-float intrinsics such as __fmaxf16 that aren't present in older macOS libSystem. It therefore also requires macOS 26.2+ at runtime; older macOS hosts should stick with the default :aot variant, whose prebuilt is built against macOS 14 and runs anywhere.

Install Emily as the global Nx backend and use Nx normally:

Nx.global_default_backend({Emily.Backend, device: :gpu})

Nx.tensor([[1.0, 2.0], [3.0, 4.0]])
|> Nx.dot(Nx.tensor([[5.0], [6.0]]))
|> Nx.to_flat_list()
# => [17.0, 39.0]

Use Emily.Compiler for defn / Nx.Serving:

Nx.Defn.global_default_options(compiler: Emily.Compiler)

Bumblebee inference works with no further configuration once the backend is installed — see the conformance suites under test/emily/conformance/ for worked DistilBERT, Qwen3, ViT, and Whisper pipelines, and the Notebooks section of the HexDocs nav for runnable Livebooks.

The low-level tensor API (Emily.from_binary/3, to_binary/1, shape/1, dtype/1, eval/1) remains available for diagnostics and direct MLX round-trips, but most users should go through Nx.

MLX dispatches GPU work through Metal command queues. Emily owns one worker thread per command queue; each worker is a dedicated OS thread that runs the MLX ops on behalf of BEAM processes. NIFs return immediately after enqueueing their work on a worker: the worker runs the op, then posts {ref, {:ok, result}} back to the caller via enif_send, and the caller's public wrapper awaits that message with a plain receive. No BEAM scheduler (regular or dirty) blocks on MLX work — callers see the same synchronous semantics as before, but the scheduler is free to run other processes while the GPU is busy.

Because the MLX stream is pinned to its worker thread, MLX's per-thread CommandEncoder state stays consistent regardless of how the BEAM migrates Elixir processes between schedulers.

By default, every op uses the default worker owned by the Emily.MlxStream.Default GenServer under the application supervisor. That single queue serialises all GPU work across the VM — correct and simple, but a bottleneck under concurrent inference.

The recommended pattern for concurrent inference: load the model once, create a pool of streams at boot, and route each request to a worker that owns one of those streams. Weights live in one MLX buffer that every stream reads; the only per-stream cost is the Metal command buffer.

# 1. Load weights once, at application start.
{:ok, model}     = Bumblebee.load_model({:hf, "Qwen/Qwen3-0.6B"})
{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-0.6B"})
{:ok, config}    = Bumblebee.load_generation_config({:hf, "Qwen/Qwen3-0.6B"})

serving =
  Bumblebee.Text.generation(model, tokenizer, config,
    defn_options: [compiler: Emily.Compiler]
  )

# 2. Start N workers; each owns one Emily.Stream for its lifetime.
defmodule MyApp.StreamWorker do
  use GenServer

  def start_link({id, serving}),
    do: GenServer.start_link(__MODULE__, serving, name: via(id))

  def run(id, input), do: GenServer.call(via(id), {:run, input}, :infinity)

  @impl true
  def init(serving) do
    {:ok, %{serving: serving, stream: Emily.Stream.new(:gpu)}}
  end

  @impl true
  def handle_call({:run, input}, _from, %{stream: s, serving: sv} = state) do
    result = Emily.Stream.with_stream(s, fn -> Nx.Serving.run(sv, input) end)
    {:reply, result, state}
  end

  defp via(id), do: {:via, Registry, {MyApp.StreamRegistry, id}}
end

# 3. Dispatch each request to any free worker (round-robin, poolboy,
#    a Registry lookup, etc.). Calls to different workers run
#    concurrently on distinct Metal command queues.
MyApp.StreamWorker.run(pick_worker(), "The quick brown fox…")

Create streams once at worker init, not per-request — Emily.Stream.new/1 spawns an OS thread.

Stream lifecycle. Emily.Stream has no explicit release API; cleanup piggybacks on BEAM GC of the NIF resource held in the struct. In the pattern above, the stream lives as long as its owning worker process: when the worker terminates (crash, supervisor shutdown, or GenServer.stop/1), the process heap is reclaimed, the resource's refcount drops to zero, and the NIF destructor joins the dedicated OS thread. A supervised restart therefore drops the old stream and allocates a fresh one in the child's init/1. To drop a stream deliberately, terminate the process that owns it.

For small models where duplicating weights is cheap, start K Nx.Serving instances behind poolboy / Registry / etc. Each instance holds its own copy of the weights. No Emily.Stream is involved, so every instance dispatches onto the same default Metal command queue — requests run sequentially at the GPU even though multiple servings exist at the BEAM level. The pool buys parallelism for CPU-side serving work (pre/post-processing, batching) but not for GPU-side compute. Memory scales linearly with K.

Combine the two if you need both: K servings for CPU parallelism, each with its own Emily.Stream for GPU parallelism.

Nx.Serving itself is stream-agnostic — it calls into Emily.Compiler which dispatches to whatever MLX stream is installed in the calling process. That gives three viable configurations:

In every case Nx.Serving.run/2 / Nx.Serving.batched_run/2 is the caller-facing API; the only difference is whether the calling process wraps the call in Emily.Stream.with_stream/2 and whether you run one serving or many.

See Emily.Stream for the API and the qwen3_quantized notebook under Notebooks for a worked multi-stream example.

Emily emits :telemetry events at the evaluation boundary ([:emily, :eval, *], [:emily, :to_binary, *]) and at every Nx.BinaryBackend fallback ([:emily, :fallback, *]). Attach a handler to graph hotspots or detect silent performance cliffs — see Emily.Telemetry for the full event catalogue.

When a backend callback has no native MLX path, Emily transparently falls back to Nx.BinaryBackend. The fallback is ~100× slower; to get a one-shot Logger.warning per {op, input_shapes} pair the first time each one fires (recommended during development):

# config/dev.exs
config :emily, :warn_on_fallback, true

The warning is off by default so library consumers and CI logs stay quiet. The telemetry event fires regardless.

Two compile-time flags re-enable runtime checks that MLX (and every other GPU backend) skips by default. Both are off by default with zero runtime cost when off — the guarded branches are dead-code eliminated by the Elixir compiler.

# config/dev.exs
config :emily,
  debug_bounds_check: true,
  debug_detect_nan_inf: true

• :debug_bounds_check — raises on out-of-range / negative indices in gather / take / take_along_axis / indexed_add / indexed_put. Catches the silent-NaN-from-OOB-gather class of bug (e.g. a vocab-30522 tokenizer paired with a tiny-random model whose embedding table is smaller).
• :debug_detect_nan_inf — scans results of matmul, the fused layer_norm / rms_norm, and both fused SDPA variants. Surfaces numerics failures at the producing op rather than downstream.

Each check is a per-op MLX reduction plus a scalar readback — a worker sync that breaks lazy-graph fusion. Leave off in release builds. See the Emily moduledoc for the full opt-in snippet.

mix test                           # fast suite (unit + property)
mix test --only conformance        # + Bumblebee tiny-random suites
mix test --only qwen3_full         # full Qwen3-0.6B checkpoint (~1.5 GB)
mix test --only qwen3_quant_full   # quantized Qwen3-0.6B end-to-end
mix test --only vit_full           # full ViT-base (~330 MB)
mix test --only whisper_full       # full whisper-tiny (~150 MB)
mix test --only training_full      # MNIST convergence canary
mix test --only soak               # memory + concurrency soak harnesses

Each layer has its own oracle: hand-computed expected values at the Native layer, Nx.BinaryBackend on the same inputs at the Backend layer, Emily.Backend in non-defn mode at the Compiler layer, and HuggingFace Transformers reference slices end-to-end. A bug can only be introduced in the layer where its test fails.

Emily stands on the shoulders of two projects without which it would not exist:

• MLX — Apple's array framework for Apple Silicon, developed by the MLX team at Apple. Emily is a thin Elixir layer over MLX's C++ API; every tensor op ultimately runs through MLX.
• EMLX — the original Elixir-Nx MLX backend by Paulo Valente and contributors. EMLX proved the integration shape between Nx and MLX, and Emily used it as a design reference and early ground-truth source.

Thanks also to Cocoa Xu, whose MLX build scripts and prebuilt-binary tooling underpin much of the macOS NIF supply chain in the Elixir ML ecosystem.

MIT