Feature
01
Selective Compression
We isolate rank reduction to intermediate layers, leaving downstream logic intact. This provides a natural shock absorber against non-linear geometric drift.
Topological Partitioning · Organic Drift Recovery
QTensor · Edge AI Inference Engine
Edge AI Inference.
Compiled analytically.
Zero-byte disk writes. Zero gradient training epochs. QTensor compresses LLMs directly to edge-native hardware using deterministic linear algebra.
* Evaluation weights are gated. Access requires manual approval and an executed Mutual Non-Disclosure Agreement (MNDA).
The Baseline
Deterministic performance verified on silicon.
3.02 GB
Sub-4GB footprint optimized for 7B edge silicon.
Compresses foundation-class models to fit entirely within edge hardware memory budgets with zero disk swaps.
54.8%
Pass@1 | Retains 65% of raw FP16 intelligence (HumanEval).
Preserves advanced algorithmic reasoning and code synthesis capability without gradient fine-tuning or quantization collapse.
< 8ms
Time to First Token (TTFT) edge latency.
Instantaneous closed-form linear algebra compression executed in single-pass compilation with zero training epochs.
Methodology: Evaluated non-mocked on Qwen2.5-Coder-7B via EvalPlus sandbox on NVIDIA RTX 5080 (54.88% Base Pass@1, 50.61% HumanEval+). Measured against raw unprompted FP16 baseline (84.8%). Full audited telemetry, exit cosine similarity maps, and Q30.130 fixed-point benchmarks are provided in the Technical Brief.
Architecture
The Engine
Deterministic linear algebra replaces heuristic backpropagation. We decompose high-dimensional weights into lean, mathematically stable edge representations.
Precision
02
Fixed-Point Accumulation
Floating-point drift destroys compressed models. Our 160-bit fixed-point math engine eliminates underflow during matrix dot products.
Accumulation: Q30.130 Fixed-Point Registers
Runtime
03
Deterministic Execution
Custom GPU kernels bypass standard cache thrashing, streaming projections directly through shared registers for sub-millisecond latency.
L1 Register Bypass · Sub-millisecond TTFT
Compilation Simulator
Inspect the compiled output.
Test how QTensor compiles complex algorithmic prompts into edge-optimized code using our deterministic Tier 1 engine.
Ready to compile
COMPILED OUTPUT PASS@1 VERIFIED
Hardware Execution Telemetry Active (72 TPS)
Initializing QTensor Tier 1...
VRAM Allocated: 2,896 MB (Success - Fits in 4GB Node)
Bypassing L2 Cache... Using Triton Fixed-Point Accumulation
def binary_search(arr: list[int], target: int) -> int:
"""
Compiled via QTensor Tier 1 Engine
Deterministic closed-form execution on edge silicon.
"""
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 -1 Engine: Tier 1 v1.0 | 2.8GB Memory Target: Industrial Edge Node (4GB) · 72 TPS Verified
Topology
Three-Tier Compression Topology
Multi-stage analytical compression pipeline preserving representational capacity without memory fragmentation.
LAYER 01
Entropy-Mapped Rank Allocation
Assigns dynamic rank budgets based on local tensor entropy.
LAYER 02
Salient Channel Calibration
Dynamically scales salient channels prior to low-bit packing to prevent weight collapse.
LAYER 03
Topological Buffer & Bridge
Scale-invariant topological buffer zones organically heal variance drift, recovering structural integrity without gradient descent.
Deployment
Ways to engage.
We work directly with edge hardware manufacturers, automotive silicon vendors, and enterprise robotics teams.
01 · Edge SDK
Pre-compiled Runtime Libraries
Direct C/C++ and Rust bindings for ARM Cortex, Apple Silicon, and NVIDIA Jetson hardware.
02 · Model Compression
Bespoke Analytical Compilations
Single-sprint mathematical decomposition of proprietary transformers with zero training compute overhead.
Deterministic Acceleration
Ready to deploy sub-3GB intelligence to the edge?
Request our comprehensive technical brief or schedule an engineering consultation with our team in Melbourne.
* Evaluation weights are gated. Access requires manual approval and an executed Mutual Non-Disclosure Agreement (MNDA).