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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 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 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 APIPOST /v1/completions- Text completionGET /v1/models- List available modelsGET /health- Service health checkWebSocket /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
- Set up Zig project structure
- Implement basic tensor operations with SIMD
- Create memory management system (arena allocators)
- Build HTTP server framework
- Apple Silicon detection via sysctl calls
- Updated to Zig 0.15.0-dev - compiles cleanly
- Benchmark suite showing current performance
- BLAS integration working - Apple Accelerate backend functional
- 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)
- 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:
# 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 for actual benchmark results showing current performance limitations and optimisation opportunities.
Development Approach
Following established 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 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 for technical details and current benchmarks.
References
- DeepZig V3 (Experimental Implementation) - Current working code
- DeepSeek V3 Paper - Original model architecture
- Zig Language - Language documentation
- Awesome Zig - Community resources
- Zig Patterns - Common idioms
- ZML - Zig Inference Stack
- LLaMA.cpp - C++ Inference Engine
- DeepZig Consciousness - Research goal/end game
Status: 🎯 EXPERIMENTAL DRAFT - Foundation compiles and runs basic operations (see benchmarks)
Vision: Foundation for advanced AI reasoning research
⚠️ Important: This is a research/development foundation with draft/base implementations. Not ready for production use.