LlamaCppEx

Elixir bindings for llama.cpp — run LLMs locally with Metal, CUDA, Vulkan, or CPU acceleration.

Built with C++ NIFs using fine for ergonomic resource management and elixir_make for the build system.

Features

• Load and run GGUF models directly from Elixir
• GPU acceleration: Metal (macOS), CUDA (NVIDIA), Vulkan, or CPU
• Streaming token generation via lazy Stream
• Jinja chat templates with enable_thinking support (Qwen3, Qwen3.5, etc.)
• RAII resource management — models, contexts, and samplers are garbage collected by the BEAM
• Configurable sampling: temperature, top-k, top-p, min-p, repetition penalty, frequency & presence penalty
• Embedding generation with L2 normalization
• Grammar-constrained generation (GBNF)
• Continuous batching server for concurrent inference
• Telemetry integration for observability

Installation

Add llama_cpp_ex to your list of dependencies in mix.exs:

def deps do
  [
    {:llama_cpp_ex, "~> 0.4.1"}
  ]
end

Prerequisites

• C++17 compiler (GCC, Clang, or MSVC)
• CMake 3.14+
• Git (for the llama.cpp submodule)

Backend Selection

mix compile                        # Auto-detect (Metal on macOS, CUDA if nvcc found, else CPU)
LLAMA_BACKEND=metal mix compile    # Apple Silicon GPU
LLAMA_BACKEND=cuda mix compile     # NVIDIA GPU
LLAMA_BACKEND=vulkan mix compile   # Vulkan
LLAMA_BACKEND=cpu mix compile      # CPU only

Power users can pass arbitrary CMake flags:

LLAMA_CMAKE_ARGS="-DGGML_CUDA_FORCE_CUBLAS=ON" mix compile

Quick Start

# Initialize the backend (once per application)
:ok = LlamaCppEx.init()

# Load a GGUF model (use n_gpu_layers: -1 to offload all layers to GPU)
{:ok, model} = LlamaCppEx.load_model("path/to/model.gguf", n_gpu_layers: -1)

# Generate text
{:ok, text} = LlamaCppEx.generate(model, "Once upon a time", max_tokens: 200, temp: 0.8)

# Stream tokens
model
|> LlamaCppEx.stream("Tell me a story", max_tokens: 500)
|> Enum.each(&IO.write/1)

# Chat with template
{:ok, reply} = LlamaCppEx.chat(model, [
  %{role: "system", content: "You are a helpful assistant."},
  %{role: "user", content: "What is Elixir?"}
], max_tokens: 200)

# Chat with thinking disabled (Qwen3/3.5 and similar models)
{:ok, reply} = LlamaCppEx.chat(model, [
  %{role: "user", content: "What is 2+2?"}
], max_tokens: 64, enable_thinking: false)

# Stream a chat response
model
|> LlamaCppEx.stream_chat([
  %{role: "user", content: "Explain pattern matching in Elixir."}
], max_tokens: 500)
|> Enum.each(&IO.write/1)

Lower-level API

For fine-grained control over the inference pipeline:

# Tokenize
{:ok, tokens} = LlamaCppEx.Tokenizer.encode(model, "Hello world")
{:ok, text} = LlamaCppEx.Tokenizer.decode(model, tokens)

# Create context and sampler separately
{:ok, ctx} = LlamaCppEx.Context.create(model, n_ctx: 4096)
{:ok, sampler} = LlamaCppEx.Sampler.create(model, temp: 0.7, top_p: 0.9)

# Run generation with your own context
{:ok, tokens} = LlamaCppEx.Tokenizer.encode(model, "The answer is")
{:ok, text} = LlamaCppEx.Context.generate(ctx, sampler, tokens, max_tokens: 100)

# Model introspection
LlamaCppEx.Model.desc(model)          # "llama 7B Q4_K - Medium"
LlamaCppEx.Model.n_params(model)      # 6_738_415_616
LlamaCppEx.Model.chat_template(model) # "<|im_start|>..."
LlamaCppEx.Tokenizer.vocab_size(model) # 32000

Server (Continuous Batching)

For concurrent inference, LlamaCppEx.Server manages a shared model/context with a slot pool and continuous batching:

{:ok, server} = LlamaCppEx.Server.start_link(
  model_path: "model.gguf",
  n_gpu_layers: -1,
  n_parallel: 4,
  n_ctx: 8192
)

# Synchronous
{:ok, text} = LlamaCppEx.Server.generate(server, "Once upon a time", max_tokens: 100)

# Streaming
LlamaCppEx.Server.stream(server, "Tell me a story", max_tokens: 200)
|> Enum.each(&IO.write/1)

Multiple callers are batched into a single forward pass per tick, improving throughput under load.

Benchmarks

Measured on Apple M4 Max (64 GB) with Qwen3-4B Q4_K_M, Metal backend (n_gpu_layers: -1).

Single-sequence generation

Continuous batching throughput

max_tokens: 32, prompt: "short"
──────────────────────────────────────────────────────────────────────────────
Concurrency  Wall time    Total tok/s  Per-req tok/s  Speedup  Avg batch
1            318ms        100.6        100.6          1.00x    1.1
2            440ms        145.5         72.7          1.45x    2.2
4            824ms        155.3         38.8          1.54x    4.5

Run benchmarks yourself:

MIX_ENV=bench mix deps.get
LLAMA_MODEL_PATH=path/to/model.gguf MIX_ENV=bench mix run bench/single_generate.exs
LLAMA_MODEL_PATH=path/to/model.gguf MIX_ENV=bench mix run bench/server_concurrent.exs

Running Qwen3.5-35B-A3B

Qwen3.5-35B-A3B is a Mixture-of-Experts model with 35B total parameters but only 3B active per token. It supports 256K context and both thinking (CoT) and non-thinking modes.

Hardware requirements

Download

# Install the HuggingFace CLI if needed: pip install huggingface-hub
huggingface-cli download Qwen/Qwen3.5-35B-A3B-GGUF Qwen3.5-35B-A3B-Q4_K_M.gguf --local-dir models/

Thinking mode (general)

:ok = LlamaCppEx.init()
{:ok, model} = LlamaCppEx.load_model("models/Qwen3.5-35B-A3B-Q4_K_M.gguf", n_gpu_layers: -1)

# Qwen3.5 recommended: temp 1.0, top_p 0.95, top_k 20, presence_penalty 1.5
{:ok, reply} = LlamaCppEx.chat(model, [
  %{role: "user", content: "Explain the birthday paradox."}
], max_tokens: 2048, temp: 1.0, top_p: 0.95, top_k: 20, min_p: 0.0, penalty_present: 1.5)

Thinking mode (math/code)

# For math and code, lower temperature without presence penalty
{:ok, reply} = LlamaCppEx.chat(model, [
  %{role: "user", content: "Write a function to find the longest palindromic substring."}
], max_tokens: 4096, temp: 0.6, top_p: 0.95, top_k: 20, min_p: 0.0)

Non-thinking mode

# Disable thinking via enable_thinking option (uses Jinja chat template kwargs)
{:ok, reply} = LlamaCppEx.chat(model, [
  %{role: "user", content: "What is the capital of France?"}
], max_tokens: 256, enable_thinking: false, temp: 0.7, top_p: 0.8, top_k: 20, min_p: 0.0, penalty_present: 1.5)

Streaming with Server

{:ok, server} = LlamaCppEx.Server.start_link(
  model_path: "models/Qwen3.5-35B-A3B-Q4_K_M.gguf",
  n_gpu_layers: -1,
  n_parallel: 2,
  n_ctx: 16384,
  temp: 1.0, top_p: 0.95, top_k: 20, min_p: 0.0, penalty_present: 1.5
)

LlamaCppEx.Server.stream(server, "Explain monads in simple terms", max_tokens: 1024)
|> Enum.each(&IO.write/1)

Qwen3.5 enable_thinking benchmarks

Measured on MacBook Pro, Apple M4 Max (16-core, 64 GB), Metal backend, n_gpu_layers: -1, 512 output tokens, temp: 0.6.

The MoE model (35B-A3B) is ~3.2x faster at generation since only 3B parameters are active per token despite the 35B total. Thinking mode only affects the prompt template, not inference speed.

Architecture

Elixir API (lib/)
    │
LlamaCppEx.NIF (@on_load, stubs)
    │
C++ NIF layer (c_src/) — fine.hpp for RAII + type encoding
    │
llama.cpp static libs (vendor/llama.cpp, built via CMake)
    │
Hardware (CPU / Metal / CUDA / Vulkan)

License

Apache License 2.0 — see LICENSE.

llama.cpp is licensed under the MIT License.