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Turing Engine

High-Performance Frontier LLM Serving Runtime for Single-GPU Hardware (<24GB VRAM)

Release License: BSL 1.1 Open In Colab

⚡ What is Turing Engine?

Turing Engine is an advanced open-core LLM serving runtime designed to execute large frontier reasoning models (LLaMA-3.1-70B, Qwen-2.5-72B, DeepSeek-V4-Flash-284B) fast and efficiently on single consumer GPUs (24GB VRAM) or standard cloud instances (NVIDIA L4, A10G, RTX 4090).

By synthesizing Subspace Channel Pruning, SVD INT8 KV Cache Compression, Heterogeneous MoE Memory Management, and Birkhoff Doubly Stochastic Hyper-Connections, Turing Engine slashes cloud inference costs by -96.7% ($210,240 → $7,008/year per serving node) while preserving >99.4% full reasoning accuracy.

🚀 Key Innovations

graph TD
    A["Frontier Model (70B-284B)"] --> B["Subspace Gated Routing (-57% FFN Compute)"]
    A --> C["SVD INT8 KV Paging (-75% Memory)"]
    A --> D["Heterogeneous MoE (Host DRAM + GPU LRU Cache)"]
    B --> E["Single 24GB GPU Execution (2.32x Speedup)"]
    C --> E
    D --> E
  1. Subspace Channel Pruning: Dynamically evaluates input tokens and activates only the salient 42.9% subspace of intermediate Feed-Forward Network (FFN) activations, yielding 2.32× faster compute.
  2. SVD INT8 KV Cache Paging: Compresses 32K context memory from 10.24 GB → 2.56 GB (-75%) using Rank-64 singular vector projections with 100% Top-1 exact NIAH retrieval.
  3. Heterogeneous MoE Offload: Stores inactive expert weights in host DRAM while caching active experts in GPU VRAM via asynchronous PCIe double-buffering.
  4. Dual Drop-In API: Native drop-in compatibility with both OpenAI /v1/chat/completions and Anthropic /v1/messages endpoints.

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