QuantumLeap: A Local LLM Inference Acceleration Framework Based on TurboQuant and ExpertFlow

QuantumLeap is a local LLM inference framework built on llama.cpp, integrating TurboQuant KV cache compression and ExpertFlow MoE optimization engine. It achieves a 130% speedup on consumer hardware—for example, running a 122B parameter model at 4.34 tokens per second on an RX 5600 XT (6GB VRAM) compared to baseline.

Local LLM deployment faces three key challenges:

1. Memory Bottleneck: FP16 models (e.g.,70B params) need ~140GB VRAM, far exceeding consumer GPUs; KV cache still consumes much memory even with quantization.
2. MoE Inefficiency: Traditional MoE expert scheduling wastes time on weight loading/switching.
3. Config Complexity: Users struggle to tune llama.cpp params (like -ngl layers, threads) for optimal performance. QuantumLeap addresses these pain points systematically.

TurboQuant (from Google ICLR2026) is a KV cache compression tech implemented in QuantumLeap:

• Pipeline: FWHT → Polar Decomposition → Angle Quantization (3.5/2.5 bit) → QJL Residual Coding.
• Compression Effect:

  Mode	Bits per Channel	Compression Ratio	Quality Loss
  TQ3 (Recommended)	3.5 bit	7.4x	Almost zero
  TQ2	2.5 bit	9.7x	Slight
  INT2	2.0 bit	16x	MSE=0.051

• Optimizations: AVX2 (CPU: FMA, prefetch, stack buffers) & CUDA (GPU: shared memory attention, fused kernels, warp reduction).

ExpertFlow Phase3 optimizes MoE inference with 5 strategies:

1. Expert Cache (75-85% hit rate reduces PCIe bandwidth).
2. Routing Predictor (74-92% accuracy preloads experts).
3. Transfer Compression (LZ77-style reduces bandwidth by 89.7%).
4. Custom GGML Backend (bypasses llama.cpp's inefficient paths).
5. Pipeline Overlap (parallelizes attention, expert compute, prefetch). Performance: RX5600 XT (6GB) runs Qwen3.5-122B-A10B at 4.34 tok/s (+130% vs Phase2). Hardware Upgrade Potential:

   Hardware	Expected Performance	Speedup vs Baseline	Cost
   6GB VRAM	4.34 tok/s	2.3x	$0
   24GB VRAM (RX7900XTX/RTX4090)	12-18 tok/s	6-9x	$900-1600
   48GB VRAM (A6000)	68-85 tok/s	15-19x	$4000-6000

QuantumLeap simplifies usage:

• Smart GPU Layer Detection: Auto calculates optimal -ngl (e.g., ngl=45 is 42% faster than manual ngl=35 for Qwen40B).
• Multi-GPU Support: Auto detects NVIDIA/CUDA, AMD/ROCm, Apple Silicon/Metal.
• Ollama Compatible API: Runs on port11435 (coexists with Ollama 11434), supports model hot-swap, streaming, OpenAI endpoints.
• Web UI: Built-in model management, workspace isolation, HuggingFace search, real-time monitoring.

Key benchmarks:

• SmolLM2-1.7B (Q4_K_M): CPU baseline=31.2 tok/s; GPU full offload=120.4 tok/s (+286%).
• Qwen40B IQ2_XXS: CPU baseline=2.07 tok/s; auto config=2.95 tok/s (+42%). These validate the effectiveness of automated configuration.

QuantumLeap is ideal for:

1. Personal Local Deployment: 6GB VRAM runs122B MoE models;24GB for most open-source models.
2. Dev/Test Environments: Ollama API integrates with IDEs (Windsurf, VSCode) for code completion.
3. Long Text Processing: TurboQuant's7.4x KV compression enables document analysis/code library understanding.
4. MoE Research: ExpertFlow provides a baseline for MoE inference optimization.

QuantumLeap pushes the boundary of local LLM inference by:

• Solving memory bottlenecks with TurboQuant.
• Unlocking MoE potential via ExpertFlow.
• Lowering entry barriers with automated config. It demonstrates that system-level optimizations can overcome consumer hardware limits, making it a valuable tool for developers/researchers aiming to deploy LLMs locally.