Zero-byte disk writes. Zero gradient training epochs. QTensor compresses foundation-class LLMs to sub-3GB edge silicon and consumer GPUs using deterministic linear algebra and subspace residual bridges.
The Baseline
Evaluated non-mocked with exact AST syntax validation, cosine similarity telemetry, and zero repetition loops.
Evaluates 4-billion parameter models entirely within consumer hardware limits without host swapping.
Retains 32.5% Pass@1 on AtCoder standard I/O.
Instantaneous closed-form linear algebra compression executed with zero gradient training epochs.
Methodology: Evaluated live across official benchmark suites: HumanEval+ (54.88% Pass@1 on Qwen2.5-Coder-7B), LiveCodeBench v6 (175 problems, 81.1% AST, 58.1% Easy Pass@1 on Gemma 4 E4B-IT), and Llama 3.2 3B (0.80 exit cosine similarity). Full audited telemetry maps and Triton kernel specifications are available in the Technical Brief.
Hardware Verification
Real measured footprints, benchmark accuracy, and target silicon across verified QTensor compilations.
| Model Architecture | Base Footprint | QTensor Footprint | Reduction | Verified Benchmark | Target Hardware | Access |
|---|---|---|---|---|---|---|
| Llama 3.2 3B QTensor v2 Protected Semantic Corridor Compilation | 6.00 GiB (BF16) | 2.98 GiB (v1) / 5.60 GiB (v2) | -50.4% / -6.7% | 0.8008 Cosine Sim
Zero Repetition Loops | ARM Cortex / Apple Silicon / 4GB Edge | |
| Qwen 2.5 Coder 7B QTensor Tier 1 Analytical Corridor Compilation | 14.2 GB (FP16) | 3.02 GB | -78.7% | 54.88% Pass@1
HumanEval+ Sandbox | 4GB Industrial Edge / Jetson Orin | |
| Gemma 4 E4B-IT QTensor Analytical Subspace Bridge Compilation | 16.0 GB (>16GB OOM) | 15.15 GB Peak VRAM | Fits 16GB VRAM | 81.1% AST Correct
58.1% Pass@1 (LiveCodeBench v6) | RTX 5080 (16GB) / Consumer GPUs | |
| Gemma 2 9B IT QTensor Closed-Form Analytical Decomposition | 18.2 GB (BF16) | 4.80 GB | -73.6% | Retains 68% FP16
Deterministic Prefill | MacBook 8GB / Workstation Edge |
Architecture
Deterministic linear algebra replaces heuristic backpropagation. We decompose high-dimensional weights into lean, mathematically stable edge representations.
Preserves critical semantic embedding spaces and logit projection boundaries. QTensor isolates rank optimization away from sensitive boundary layers while maintaining 100% native attention mechanics across the network.
Standard low-rank factorization causes severe activation collapse and repetitive looping. QTensor employs proprietary analytical residual bridging to dynamically compensate for non-linear energy loss, eliminating repetition loops on long-context code generation.
Floating-point drift degrades compressed models over extended context windows. QTensor's proprietary execution kernels run deterministic accumulation directly on silicon, maximizing cache locality and minimizing time-to-first-token (TTFT).
Compilation Simulator
Test how QTensor compiles complex algorithmic models into edge-optimized silicon execution using our deterministic engine.
def binary_search(arr: list[int], target: int) -> int:
"""
Compiled via QTensor Edge Engine (Zero-Shot Deterministic Pass)
Verified Silicon Execution | 0.0% Repetition Loops
"""
left, right = 0, len(arr) - 1
while left <= right:
mid = (left + right) // 2
val = arr[mid]
if val == target:
return mid
elif val < target:
left = mid + 1
else:
right = mid - 1
return -1Model Verification
Inspect verified pre-compiled QTensor models with gated safetensors available under MNDA, model cards, and reproducible evaluation configs.
Topological buffer expansion with analytical corridor compilation. Full AST compliance and 0% repetition loops.
Sub-4GB edge compilation retaining 54.88% Pass@1 on HumanEval+. Zero gradient training.
Closed-form analytical subspace decomposition. Fits 8GB MacBooks and edge devices with high semantic retention.
Enterprise edge compilation for autonomous and embedded systems. High-entropy channel calibration.
ZeroFold Engineering Practice
QTensor's core compiler is supported by ZeroFold's engineering practice. We deliver custom kernel optimization, edge hardware integration, and sovereign production pipelines for enterprise, defense, robotics, and embedded systems.
Deterministic Acceleration
Request our comprehensive technical brief or schedule an engineering consultation with our team in Melbourne.
* Evaluation weights and proprietary Triton kernel sources are released under Enterprise MNDA.