Threading is hard.
Developers spend weeks manually optimising threads, fighting race conditions, and debugging deadlocks.
The Language That Parallelises Everything.
Write sequential code. FORGE makes it parallel.
Automatically. At compile time. Zero effort.
Threading is hard.
Developers spend weeks manually optimising threads, fighting race conditions, and debugging deadlocks.
GPU programming is complex.
GPUs need separate CUDA, Metal, or Vulkan code — completely different codebases for CPU and GPU paths.
Memory is wasted.
Streaming 50GB files causes memory spikes. Zero-copy requires manual mmap and pointer arithmetic.
Write @parallel once.
One annotation. The compiler distributes your code across ALL CPU cores. Automatically. No thread pools. No mutexes.
@gpu annotation → auto GPU dispatch.
Annotate once. FORGE generates CUDA, Metal, or Vulkan automatically. No separate GPU codebase needed.
@stream + @zero_copy → 9.47 GB/s.
Stream 50GB files with zero copies, zero memory spikes. Built-in zero-copy I/O at operating system level.
| Feature | FORGE | C++ | Rust | Go | Python |
|---|---|---|---|---|---|
| Auto-parallelism | ✓ | ✗ | ✗ | ✗ | ✗ |
| GPU offload | ✓ | ✗ | ✗ | ✗ | ✗ |
| Zero-copy streams | ✓ | ✗ | ⚠ | ✗ | ✗ |
| Zero GC pauses | ✓ | ✓ | ✓ | ✗ | ✗ |
| Game-first ECS | ✓ | ⚠ | ✗ | ✗ | ✗ |
| Compile speed | ✓ | ✗ | ✗ | ✓ | N/A |
| Learning curve | Low | High | High | Med | Low |
ECS runs @parallel across all cores. Zero GC stutters. Deterministic compile-time scheduling for real-time performance.
Neural nets with @gpu auto-offload. Zero CUDA boilerplate. Deploy ML without touching GPU code.
9.47 GB/s streams. 212KB HTTP servers. Real-time OS-level performance. Zero-copy I/O built in.
FORGE is positioned as the systems language for the AI era. A generational opportunity to own the next systems programming standard.
Current Phase: Compiler + Runtime + IDE + Package Registry
Valuation Range: $200M – $2B
📧 Contact for Acquisition Deck