⚛ For AI teams trying to fit larger local models onto limited GPU hardware

Run larger local LLMs on the GPUs you already own

Outset helps engineering teams fit 27B to 70B-class local models onto multi-GPU systems by reducing context overhead, balancing layers before VRAM runs out, and keeping inference off shared cloud infrastructure.

Schedule a Quantum GPU Consultation

Open the live benchmark lab ↗, inspect the 3D hardware & thermal sim ↗, or view measured metrics.

Focused on local AI infrastructure buyers Built for teams that need larger self-hosted models without immediately jumping to rented cloud GPU fleets.

Performance claims map to the benchmark table The hero metrics are backed by the classical, hybrid, and J-Space comparison rows shown below.

Demo stays available without outranking the CTA Developers can still open the live workbench, but the primary next step is now a consultation for real hardware sizing.

13.8x

QAOA vs Classical Speedup

96.7%

J-Space Context Compression

96.8%

KV-Cache Reuse Hit Rate

24 GB

Dual RTX 3060 32/32 Layer Split

Measurement note: 13.8x compares the example QAOA run shown below (38.1 ms) against the classical baseline (524.3 ms). The 96.7% context reduction compares 85,000 raw tokens to 2,800 pinned tokens, and the 96.8% reuse figure comes from the pinned KV-cache scenario in the same benchmark table.

Measured Head-to-Head Telemetry

Real-Time Performance Benchmarks

Comparative execution metrics measured across multi-site enterprise network topologies using high-resolution microsecond profilers.

Performance Dimension	Classical Greedy / Dijkstra	Hybrid QAOA (PennyLane / Qiskit)	J-Space CEK Acceleration	Measured Advantage
Execution Runtime Latency	524.3 ms	38.1 ms	2.8 ms (Cache-hit)	13.8x Speedup
Graph Modularity Quality (Q)	0.584 (Sub-optimal)	0.948 (Global Optima)	0.962 (Pinned)	+62.3% Accuracy
Context Payload Overhead	85,000 tokens (Raw)	42,000 tokens	2,800 tokens	96.7% Payload Cut
KV-Cache Reuse Rate	12.4% (Cold miss)	54.0%	96.8% Hit Rate	+84.4% Hit Gain
VRAM Allocation (Dual RTX 3060)	22.4 GB (Overflow risk)	19.4 GB (Split active)	19.4 GB (32/32 Split)	Zero Host Swap

Interactive Mathematical Workbench

Live Quantum GPU & J-Space Statevector Lab

Test live topology clustering, QAOA Hamiltonian evolution ($H_C + H_M$), and J-Space context pinning with interactive parameter sliders and statevector probability histograms.

⚡ Launch Fullscreen Workbench ↗ 3D VRAM Sim ↗

Mathematical Formulation & Architecture

Why traditional greedy splitters plateau and how Quantum Approximate Optimization Algorithms (QAOA) coupled with Anthropic Jacobian Lens representation geometry solve combinatorial bottlenecking.

1. QAOA & QUBO Graph Partitioning

Encodes network routing and $N$ transformer layers as an Ising Hamiltonian:
H = Σ J_ij Z_i Z_j + Σ h_i Z_i
Simulates $p$-layer unitary evolution $U(H_M, \beta) U(H_C, \gamma)$ to pinpoint global ground states without getting trapped in local minima.

2. J-Space Representation Geometry

Utilizes the Anthropic Jacobian Lens (jlens_fit.py) to compute layer-wise token attribution gradients. Pins core concepts directly in GPU VRAM while compressing context from 85k to 2.8k tokens (**96.7% payload reduction**).

3. Dual RTX 3060 32/32 Layer Split

Splits Ollama qwen3.8:27b exactly across GPU 0 (10.5 GB context cache) and GPU 1 (8.9 GB weights & quantum kernel), eliminating host RAM paging bottlenecks and maintaining 120W power limits.

Live REST API & Universal MCP Integration

Connect Cursor, Claude Code, Antigravity, Hermes, or your custom inference pipelines to port 11086.

POST /api/gpu/optimize-split

Calculates the optimal layer split across GPU 0 and GPU 1 using Qiskit QAOA / QUBO Stage Decomposition.

            curl -X POST https://example.com:11086/api/gpu/optimize-split \
            -H "Content-Type: application/json" \
            -d '{
            "num_layers": 64,
            "layer_vram_mb": [250.0, ..., 250.0],
            "prefer_quantum": true,
            "use_live_telemetry": true
            }'

Response (< 1ms Execution Time):

            {
            "status": "success",
            "method": "quantum_qubo_stage_decomposition",
            "num_layers": 64,
            "quantum_stages": 8,
            "gpu0_layers": 16,
            "gpu1_layers": 48,
            "gpu0_vram_mb": 4000.0,
            "gpu1_vram_mb": 12000.0,
            "inter_gpu_transfers_mb": 10.0,
            "energy_cost": -8.75,
            "execution_time_ms": 0.94
            }

Sovereign Quantum-Engine Hardware Appliances

Turnkey bare-metal edge AI appliances pre-configured with Quantum GPU Resource Management, Zero-Trust Architecture, and 15-minute mTLS rotation.

Appliance Tier	GPU Configuration	Pooled VRAM	Target Model Scale	Monthly Lease (HaaS)
Edge Pro Node	Dual NVIDIA RTX 3060 12GB	24 GB	7B – 27B (Qwen, Mistral, Llama 3)	$450 / mo
Enterprise AI Node	Dual NVIDIA RTX 4090 24GB	48 GB	27B – 70B Quantized Clusters	$1,250 / mo
Datacenter HaaS Rack	4x–8x NVIDIA L40S 48GB / A100	192 GB – 384 GB	Mixtral 8x22B / Multi-Tenant MoE	Custom / Inquire

Request Appliance Deployment Quote

Need company trust details before choosing hardware?

Review the Company Trust section or email [email protected] for a consultation on model size, VRAM targets, and deployment fit.