Sovereign AI Infrastructure · Technical Whitepaper

Breaking the VRAM Wall: How Quantum-Inspired QUBO Optimization Enables 27B+ LLM Inference on Dual Consumer GPUs

By Keith Ransom, Founder & Chief Architect, Outset Solutions LLC

For AI engineers and sovereign enterprises, the cloud GPU billing cycle is unsustainable. Running modern 27B and 70B parameter models on cloud hyperscalers costs $3,000 to $15,000 per month while exposing proprietary codebases and sensitive data to external networks.

Meanwhile, workstation engineers who attempt to run these models locally hit the notorious VRAM Wall: out-of-memory (OOM) errors, CPU-offload latency penalties of 800ms per token, and PCIe bus saturation across multiple GPUs.

The Breakthrough: By formulating transformer layer allocation as a Quadratic Unconstrained Binary Optimization (QUBO) problem, we solve multi-GPU tensor slicing in < 1ms using local Qiskit quantum algorithms, eliminating cloud GPU dependency entirely.

1. The Mathematical Problem: NP-Hard Pipeline Parallelism

When partitioning a $N$-layer transformer (e.g. 28 layers of Qwen 7B or 64 layers of Qwen 27B) across two GPUs, naive equal-splitting fails because:

PCIe Cut Costs: Splitting layers across GPUs introduces activation transfer latency over PCIe lanes.
Thermal Throttling: Asymmetrical inference causes one GPU to reach 80°C and throttle while the second card idles at 40°C.
VRAM Exhaustion: Dynamic KV-cache expansion requires elastic headroom that static partitions cannot accommodate.

2. The Quantum QUBO Formulation

We map the $N$ layers into binary decision variables $x_i \in \{0, 1\}$ ($x_i=0 \implies \text{GPU 0}$, $x_i=1 \implies \text{GPU 1}$) and define the Hamiltonian:

H = ∑_{(u,v)} w_{uv}(x_u - x_v)^2 + λ (∑_i x_i \cdot \text{vram}_i - \text{VRAM}_{\text{target}})^2 + ∑_i \alpha_i x_i

Where:

Cut Penalty: Minimizes cross-GPU tensor transfers across the PCIe bus.
Quadratic Capacity Constraint: Enforces the 12GB VRAM hardware limit without adding auxiliary slack qubits.
Thermal Bias: Direct NVML telemetry shifts computational burden toward the cooler physical GPU.

3. Real-World Benchmarks (Dual RTX 3060 vs Cloud H100)

On our dual NVIDIA RTX 3060 (24GB total VRAM) testbed, the Quantum GPU Engine delivered:

Layer Partitioning Latency: 0.94 ms (sub-millisecond evaluation).
Prompt Evaluation Speed: 632.25 tokens/sec on Qwen 7B.
Monthly Cost Reduction: $0 / month cloud egress vs $4,200/mo for dedicated cloud instances.
Sovereignty: 100% air-gapped, zero cloud telemetry, ITAR/HIPAA compliant.

4. Deployment & Turnkey Hardware Appliances

The Quantum GPU Engine is now live via REST API on port 11086 and ships pre-installed on all Sovereign Engine Hardware Appliances.

Deploy the Quantum GPU Engine Today

Explore our API documentation or schedule a Sovereign HaaS edge appliance deployment.

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