MEG — PUBLIC TECHNICAL OVERVIEW

Smarter compute.
Measured, not assumed.

MEG is a compute orchestration and optimization system focused on finding, validating and activating better execution paths for specific workloads on real hardware.

◎

MeasureReal workloads.
Real hardware.

OptimizeOnly where evidence
supports it.

⬡

ProtectQuality and stability
before promotion.

Explore Results → See Validation Get involved

Performance
Measured

Quality
Equivalent

Stability
Gated

Evidence
First

500+

Validation TestsLarge current regression suite.

Current FailuresIn the reported validation run.

504

Tests Passing0 failures in current validation.

✓

Physical EvidenceValidated on real local hardware.

STRONGEST VALIDATED LOCAL RESULTS

Performance where the data proves it.

These results come from different workloads and layers. They are intentionally shown separately and must not be combined into a single “overall MEG percentage”.

✓ Primary workstation i9‑14900HX · RTX 4060 Laptop GPU · Windows 11

GPU WORKLOAD

+53.43%

Throughput

Same-GPU exhaustive SHA‑256d comparison, with quality-equivalent execution.

−34.82% p50 wall −30.65% paired NVIDIA-device energy

EXACT REPEATED WORK

9.71×

CPU / RAM

High-commonality registered transform with exact output equivalence.

7.64× CUDA / VRAM 256 → 26 physical operations

REPEATED-DATA WORKLOAD

+36.72%

Recurring throughput

Three independent repeated-input sessions with exact outputs.

−26.86% p50 −28.47% p95

ADAPTIVE EXECUTION

Validated repeated-input execution

Workload-specific execution with exact output equivalence.

−22.95%p50 −23.90%p95 +29.78%throughput

BLENDER · 6.59M TRIANGLES

Physical high‑poly repair

Full-capability A/B with byte-identical outputs.

−17.23%p50 −0.67%p95 −3.06%peak RSS

LARGE-MESH PREPROCESSING

Large-mesh preprocessing

Exact full coverage across all 6,593,072 triangles and partition totals.

11.76×faster −91.5%wall time 24.091 → 2.048 sbaseline → optimized

FULL-PC ARCHITECTURE TESTING

MEG was tested as a system, not only as isolated code.

The Full-PC work examines how compute, memory, transfers, scheduling, operating-system constraints and supporting I/O interact as one execution architecture. Physical measurements establish the anchors; calibrated architecture searches explore safe combinations before any candidate is eligible for physical promotion.

MEG FULL-PC coordinated execution

CPUcompute · scheduling

RAMmemory · throughput

GPUcompute · acceleration

VRAMbandwidth · transfers

I/Ostorage · connectivity

OSoperating system · runtime

Powerthermals · sustained load

Displaysystem contention

PHYSICAL

Measured workstation capability profile

The primary workstation was profiled so MEG could ground device, memory and routing decisions in the actual machine rather than generic assumptions.

793CPU GFLOPS

7.1GPU TFLOPS

29.9RAM GiB/s

209VRAM GiB/s

~9× GPU / CPU compute ~7× VRAM / RAM bandwidth

PHYSICAL MECHANISM

CPU/RAM + CUDA data-path optimization

Component-level processing and data-movement improvements were tested physically with exact outputs before they were accepted as useful optimization evidence.

6.63×CPU/RAM primitive

6.72×CUDA device path

Component-level physical result — not presented as a universal whole-application gain.

CALIBRATED FULL-PC SEARCH

Searching the complete safe architecture space.

These campaigns are architecture simulations/digital twins calibrated with physical measurements. They identify what should be physically tested next; they are not labeled as new physical speedups.

CALIBRATED SIMULATION

207,360

Safe configurations enumerated

Full-PC search spanning execution, memory, CPU policy, thermal state and control-path choices.

14.7M finalist-state evaluations 0% modeled >1% regression probability for the safe winner

CALIBRATED SIMULATION

+5.25%

Short-class safe winner

Modeled median improvement over the physical short-path anchor after coordinating the Full-PC safe path.

1.601 GH/s modeled p50 +6.85% sustained p50 vs recent live anchor

EXTENDED FULL-PC SEARCH

1.87M

Deployable-safe combinations

A broader Full-PC search evaluated additional whole-system coordination and I/O combinations.

22.4M finalist-state evaluations +6.07% modeled short median +7.51% modeled sustained median

WHAT THE FULL-PC WORK EXAMINED

Optimization across the machine.

01GPU / VRAMCompute performance, memory bandwidth and data movement.

02CPU / Hybrid coresCompute scaling, scheduling and background system work.

03RAM / memoryMemory throughput, capacity pressure and workload behavior.

04PCIe / ChipsetSystem interconnect and data-transfer behavior.

05Power / ThermalSustained-state behavior while preserving safe limits.

06Windows / Windows runtimeOperating-system scheduling and desktop contention.

07NVMe / StorageStorage latency and throughput where relevant.

08NIC / NetworkLatency, jitter and control traffic without mislabeling it compute throughput.

✓

Evidence boundary

The public claim is not “MEG makes the whole PC X% faster.” The validated claim is that MEG profiles and evaluates the whole machine, validates improvements with physical tests, then uses Full-PC architecture studies to decide which safe combinations deserve controlled physical qualification.

CURRENT VALIDATION STATE

Correctness before acceleration.

The current checkout was revalidated on 22 August 2026. Performance claims are retained from controlled physical A/B reports rather than rerun under concurrent GPU load.

Full unit discovery 504 PASS 6 expected skips · 0 fail

Recursive package imports 197 PASS Current sandbox package tree

Python compileall PASS Current checkout

Core focused evaluation 15 / 15 All focused checks pass

Focused quality checks PASS Current validation run

Core retrieval p50 / p95 1.76 / 2.84 milliseconds

Performance proven

Physical A/B, equivalent output or quality, repeated samples and relevant promotion gates.

Mechanism proven

The mechanism executes correctly, but no universal application-level speedup is implied.

Provisional

Physical evidence exists, but sample balance or environment control is not yet sufficient.

Rejected when needed

Candidates that fail latency, quality or stability gates remain unpromoted.

WHY REJECTION MATTERS

Not every “optimization” is accepted.

MEG’s evidence model includes negative results, because rejecting a regression is part of the validation process.

Performance regressionSlower median result → rejected

Marginal candidateInsufficient improvement → rejected

Unbalanced candidateTail latency regressed → rejected

Unstable candidateRepeatability gate failed → rejected

WHAT'S NEXT

Future Plans

1 Scale

More hardware classes, larger models, longer contexts and broader real-world workloads.

2 Repeat

Increase independent A/B repetitions and thermal/environmental control.

3 Harden

Expand fault tolerance, regression coverage, rollback and long-running stability.

4 Integrate

Validate more real external runtimes without weakening quality or safety boundaries.

5 Publish

Release more reproducible public evidence while keeping proprietary implementation protected.

Our Philosophy

We don't add percentages from unrelated workloads.
We don't accept an optimization because it looks faster once.
We measure, verify and keep the baseline when evidence says we should.

“
Performance is not claimed.
It is measured.
— MEG

Smarter compute.Measured, not assumed.