DeepSeek V3 in Zig
Language: Zig License: DeepSeek Status: Proposal
Performance: High Efficiency Platform: Cross Platform
Feature: SIMD Optimized Architecture: MoE Backend: Customizable
# DeepZig V3: A High-Performance LLM Architecture ## Overview A **DRAFT proposal & foundation** for implementing DeepSeek V3 in Zig to create a high-performance, web-ready LLM inference engine. This leverages Zig's unique advantages for systems programming while targeting modern deployment scenarios. **⚠️ Status: EXPERIMENTAL DRAFT** ✅ **Foundation compiles with Zig 0.15.0-dev**, including: - ✅ HTTP server framework (basic structure) - ✅ SIMD-optimized tensor operations (draft implementation) - ✅ Cross-platform backend architecture - ✅ Initial memory management - ✅ **Apple Silicon M-series detection** (hardware detection via sysctl) - ✅ Comprehensive build system draft - ✅ **BLAS integration working** (Apple Accelerate backend functional) - ✅ **Improved matrix operations** (1000+ GFLOPS performance on an M1 Macbook) - ⚠️ **NOT PRODUCTION READY** - Draft implementation for research/development **Performance Update**: ~~Current naive algorithms are ~1000x slower than optimized BLAS~~ **BLAS integration now functional.** Matrix multiplication: **2.1ms for 1024×1024** at **1000+ GFLOPS** on an M1 Macbook. This represents significant improvement over our initial naive implementation. See [experimental benchmarks](experimental/README.md#benchmarks) for detailed performance data. ## Why This Matters Current LLM inference is dominated by Python/PyTorch, which introduces: - **Garbage collection pauses** during generation - **Runtime overhead** from dynamic dispatch - **Complex deployment** with heavy runtimes - **Platform lock-in** due to dependency complexity **Progress Update**: Our draft implementation now includes BLAS integration delivering improved matrix operation performance with Apple Accelerate backend. ## Expected Benefits vs Current Reality | Aspect | Current (PyTorch) | Target (Zig) | **Current Achievement** | |--------|------------------|--------------|-------------------------| | Cold start | 10-30s | **< 2s** | *Not measured* | | Memory usage | 20-40GB | **< 16GB** | *16GB+ for basic ops* | | Dependencies | ~2GB runtime | **Single binary** | ✅ **Single binary** | | Deployment | Complex | **Copy & run** | ✅ **Copy & run** | | Matrix Mul (1024×1024) | ~1ms (optimized) | **< 1ms** | ✅ **2.1ms (1000+ GFLOPS)** | *See [experimental benchmarks](experimental/README.md#benchmarks) for current performance measurements.* ## Why Zig? **Performance**: Zero-cost abstractions, compile-time optimization, direct hardware access
**Simplicity**: Single static binary, no runtime dependencies, cross-compilation built-in
**Web-First**: Native HTTP server, WebAssembly compilation, efficient memory management ## Proposed Architecture ``` ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ Web Layer │ │ Core Engine │ │ Backends │ │ │ │ │ │ │ │ ├─ HTTP API │◄──►│ ├─ Transformer │◄──►│ ├─ CPU (SIMD) │ │ ├─ WebSocket │ │ ├─ Attention │ │ ├─ Metal (macOS)│ │ ├─ Rate Limit │ │ ├─ MoE Routing │ │ ├─ CUDA (Linux) │ │ └─ Auth │ │ └─ Tokenizer │ │ └─ WebGPU │ └─────────────────┘ └──────────────────┘ └─────────────────┘ ``` ## Draft Web API Framework ### Planned Endpoints (Basic Structure Implemented) - `POST /v1/chat/completions` - OpenAI-compatible chat API - `POST /v1/completions` - Text completion - `GET /v1/models` - List available models - `GET /health` - Service health check - `WebSocket /ws` - Streaming inference (planned) ### Deployment Vision - **Static binaries** - Single file deployment, no dependencies - **Direct VPS deployment** - Copy binary and run with systemd - **Edge devices** - ARM/RISC-V cross-compilation - **Serverless functions** - Minimal cold start with static linking - **WebAssembly** - Browser inference without additional runtime ## Implementation Plan Status ### Phase 1: Foundation ✅ **DRAFT COMPLETE** - [x] Set up Zig project structure - [x] Implement basic tensor operations with SIMD - [x] Create memory management system (arena allocators) - [x] Build HTTP server framework - [x] **Apple Silicon detection via sysctl calls** - [x] **Updated to Zig 0.15.0-dev - compiles cleanly** - [x] **Benchmark suite** showing current performance - [x] **BLAS integration working** - Apple Accelerate backend functional - [x] **Improved matrix performance** - 1000+ GFLOPS operations *📈 Performance improvement achieved - BLAS acceleration now working* ### Phase 2: Core Model (IN PROGRESS) - [ ] Implement transformer layers - [ ] Add Multi-Head Latent Attention (MLA) - [ ] Build Mixture of Experts (MoE) routing - [ ] Create tokenizer integration ### Phase 3: Backends (PLANNED) - [ ] Optimize CPU backend with AVX/NEON - [ ] Integrate Metal for Apple Silicon - [ ] Add CUDA support for NVIDIA GPUs - [ ] Implement WebGPU for browsers ### Phase 4: Web Integration (DRAFT STRUCTURE) - [x] Complete HTTP API implementation (basic structure) - [ ] Add WebSocket streaming - [ ] Build authentication/rate limiting - [ ] Create deployment tooling ## Technical Challenges - **Model Complexity**: DeepSeek V3's MoE architecture requires careful memory management - **Backend Integration**: Need efficient FFI to CUDA/Metal while maintaining performance - **Web Scale**: Handle concurrent requests without blocking inference - **Accuracy**: Match PyTorch numerical precision - **Performance**: Matrix operations now use BLAS acceleration - focus shifts to model architecture optimisation ## Platform-Specific Opportunities ### Apple Silicon (M-Series) ✅ **Draft Detection Implemented** - **Metal Performance Shaders** integration for matrix operations - **AMX instruction set** access for accelerated linear algebra - **Unified memory architecture** exploitation for zero-copy transfers - **Power efficiency tuning** across P and E cores - **✅ Proper M1/M2/M3/M4 detection** via system calls *Current status: Hardware detection working, GPU acceleration not yet implemented.* ### x86_64 Architecture - **AVX-512 vectorization** with masked operations - **Cache-friendly memory layouts** for L1/L2/L3 optimization - **NUMA-aware allocation** and thread assignment - **Dynamic dispatch** based on runtime CPU feature detection ### NVIDIA GPUs - **CUDA integration** via efficient FFI bindings - **Tensor Core utilization** for mixed-precision operations - **Custom kernels** for attention mechanisms - **Memory pooling** for reduced allocation overhead ## Getting Started **Current Status**: This repository contains a **DRAFT EXPERIMENTAL** Zig implementation foundation. ### For the Current Zig Implementation: ```bash # Clone this repository git clone https://github.com/Triex/DeepZig-V3 cd DeepSeek-V3-Zig/experimental # Build and test the foundation zig build # Run the HTTP server (basic structure) zig build run -- --port 8080 # Run benchmarks (see actual performance) zig build bench # Test Apple Silicon detection zig build-exe src/test_m_series.zig -I src -lc -framework Metal -framework Foundation ./test_m_series ``` **📊 Performance Reality Check**: See [experimental/README.md](experimental/README.md) for actual benchmark results showing current performance limitations and optimisation opportunities. ## Development Approach Following established [Zig patterns](https://github.com/SuperAuguste/zig-patterns): - **Arena allocators** for request-scoped memory - **Error unions** for explicit error handling - **Comptime generics** for zero-cost abstractions - **SIMD vectors** for numerical computation Reference: [Zig Cookbook](https://zigcc.github.io/zig-cookbook/) for implementation patterns. ## Seeking Contributors This is an ambitious **DRAFT project** that would benefit from expertise in: - **Performance optimization** (focus on transformer and attention mechanisms) - **Zig systems programming** - **GPU kernel optimization** (CUDA/Metal) - **ML model implementation** - **Web server development** - **Hardware-software co-design** - **Novel inference techniques** (Speculative decoding, quantization) ## Current Limitations & Next Steps **🚧 What's Working**: ✅ Compiles, runs, **BLAS acceleration functional** **⚠️ What's Missing**: Robust flows, actual DeepSeek V3 model implementation **📊 Performance Status**: ✅ **Matrix operations improved** (BLAS working) **🎯 Next Priority**: DeepSeek V3 transformer architecture and attention mechanisms See [experimental implementation](experimental/) for technical details and current benchmarks. ## References - [DeepZig V3 (Experimental Implementation)](experimental/) - **Current working code** - [DeepSeek V3 Paper](https://arxiv.org/abs/2412.19437) - Original model architecture - [Zig Language](https://ziglang.org/) - Language documentation - [Awesome Zig](https://github.com/C-BJ/awesome-zig) - Community resources - [Zig Patterns](https://github.com/SuperAuguste/zig-patterns) - Common idioms - [ZML](https://github.com/zml/zml) - Zig Inference Stack - [LLaMA.cpp](https://github.com/ggerganov/llama.cpp) - C++ Inference Engine - [DeepZig Consciousness](https://github.com/Triex/DeepZig-Consciousness) - Research goal/end game --- **Status**: 🎯 **EXPERIMENTAL DRAFT** - Foundation compiles and runs basic operations ([see benchmarks](experimental/README.md#benchmarks))
**Vision**: Foundation for advanced AI reasoning research **⚠️ Important**: This is a **research/development foundation** with draft/base implementations. Not ready for production use.