Your models.
Distilled to brilliance.
Six layers of precision refinement that make your AI smaller, faster, and smarter — without hallucination risk, capability loss, or hardware lock-in.
You're burning crude oil
in a fusion reactor
Modern AI models are over-engineered for most deployments. Your production stack pays a compound tax on every unnecessary parameter.
Runaway inference costs
Unoptimized models burn GPU budget on parameters that never fire in production. 40–70% of compute is wasted on dormant weights.
Latency that kills UX
Bloated models fail SLA targets under load. Users abandon requests after 200ms. Your model responds in 1.4 seconds.
Hardware lock-in
Models optimized for A100s refuse to run on ARM edge devices or CPUs without degraded performance and re-training cycles.
Quantization hallucinations
Naive compression methods break reasoning chains. Your distilled model confidently hallucinates where the original was certain.
Deployment complexity
Managing multiple model variants for different hardware targets creates an ops nightmare with no single source of truth.
Re-training treadmill
Every model update requires re-running the entire compression pipeline from scratch. Weeks of GPU time re-spent on solved problems.
Six layers of precision refinement
Each layer targets a specific inefficiency class. Together they form a complete purification system — not a compression hack.
Layer 1: Analyze — Weight Topology Mapping
Before touching a single weight, DisTillux performs full topological analysis of the model graph. We identify attention head redundancy, dead neurons, and activation sparsity patterns across 1,000+ representative inputs.
- Layer-wise relevance propagation (LRP) attribution
- Cross-layer neuron correlation matrix computation
- Dynamic activation frequency histograms
- Gradient saliency estimation without full fine-tuning
Layer 2: Distill — Knowledge Transfer
The teacher model's internal representations are transferred to the student using multi-level intermediate distillation — not just output logit matching. Reasoning pathways are preserved at the hidden-state level.
- Attention map alignment across compressed layers
- Intermediate feature distillation (not just last-layer)
- Soft-label temperature scaling with dynamic annealing
- Task-specific loss weighting for downstream fidelity
Layer 3: Refine — Structured Pruning
Structured pruning removes entire attention heads and MLP rows rather than individual weights, enabling real hardware speedups (not theoretical FLOP reductions). The pruning mask is informed by the Analyze layer.
- Head-wise importance scoring with Taylor expansion
- Block-sparse pattern generation for kernel compatibility
- Iterative magnitude + gradient joint pruning
- Automatic recovery fine-tuning on pruned structures
Layer 4: Optimize — Quantization-Aware Training
QAT simulates quantization noise during training so the model learns to work within lower bit widths. We support INT8, INT4, FP8, and mixed-precision per-tensor schemes — no post-training accuracy cliff.
- Straight-through estimator (STE) gradient propagation
- Per-channel / per-tensor / per-block quantization
- GPTQ and AWQ integration for LLM weight quantization
- KV-cache memory pressure optimization (sub-8ms rehydration)
Layer 5: Deliver — Format Packaging
The refined model is compiled and packaged for every target hardware in parallel: ONNX, TensorRT, GGUF, CoreML, OpenVINO, and more. A single distillation run yields deployment-ready artifacts for every target.
- ONNX graph optimization (constant folding, op fusion)
- TensorRT engine compilation with dynamic batch shapes
- GGUF Q4/Q5/Q8 quantization for CPU/edge deployment
- CoreML neural engine compilation for Apple Silicon
Layer 6: Evolve — Continuous Calibration
Production traffic is continuously sampled to detect distribution drift. When accuracy degradation is detected above threshold, the calibration dataset is automatically enriched and a delta-distillation is triggered.
- Online performance monitoring with latency / accuracy telemetry
- Automatic drift detection via KL-divergence sampling
- Delta-distillation: re-run only affected layers
- Versioned model registry with rollback-in-one-click
Smaller and Smarter
Compression without intelligence loss. Every DisTillux output is benchmarked against the teacher model on your task-specific eval suite before delivery.
▼ Before DisTillux
✦ After DisTillux
Five proprietary methods.
One unified pipeline.
Each technique is independently peer-reviewed and validated across 200+ model architectures.
Multi-Level Feature Distillation
Teacher knowledge is transferred at every transformer block — not just the final output. Intermediate attention patterns and hidden states are preserved, maintaining the model's reasoning chains under compression.
Sparse Gradient Magnitude Pruning
Pruning masks are computed jointly with QAT, not in post-processing. This co-optimization ensures pruned paths don't create accuracy cliffs that quantization later amplifies — the root cause of hallucinations in naive pipelines.
Mixed-Precision QAT with STE
Quantization-Aware Training with Straight-Through Estimators teaches the model to operate within INT4/INT8 bounds during training itself — not as an afterthought. Dynamic precision promotion preserves high-stakes activations.
Sub-8ms KV Rehydration Engine
DisTillux's novel KV-cache compression format enables sub-8ms cold-start rehydration from NVMe, eliminating the latency penalty of large context windows on edge deployments and serverless inference.
Attention-Guided Bit Width Selection
Not all layers deserve equal compression. DisTillux dynamically assigns higher bit widths to layers with high inter-token attention entropy and lower widths to near-uniform attention heads — automatically, per input distribution.
Why DisTillux wins
Head-to-head comparison on LLaMA-3-8B distilled to a 1.5B student model, evaluated on MMLU, HellaSwag, and TruthfulQA.
| Metric | DisTillux | Typical Compressor | Manual Pipeline |
|---|---|---|---|
| MMLU accuracy retention | 99.8% | 96.1% | 91.4% |
| TruthfulQA delta | −0.2% | −4.7% | −12.3% |
| Size reduction ratio | 8.7× | 6.2× | 5.1× |
| Latency improvement | 16.3× | 9.8× | 7.4× |
| Distillation pipeline runtime | 4.2 hrs | 11.8 hrs | 32+ hrs |
| Output formats supported | 8 formats | 3 formats | 1 format |
| Hardware targets | GPU, CPU, ARM, TPU, FPGA, Edge | GPU only | Manual |
| KV-cache rehydration | Sub-8ms | Not supported | Not supported |
| Auto calibration / drift detection | ✓ Included | ✗ | ✗ |
| Hallucination safety guard | ✓ Layer 2+3 co-opt | ✗ | ✗ |
Fleet-Wide Optimization with
Live ROI Intelligence
DisTillux doesn't just optimize one model — it sweeps your entire AI model fleet across the datacenter and continuously tracks the financial impact. One board-ready number that proves its value every single day.
The $4,999 entry price becomes a rounding error next to millions in quarterly infrastructure savings.
See Your Fleet's Savings Potential →They compress.
We optimize AND prove it.
Free open-source tools and basic compression software give you smaller models. DisTillux gives you proven, production-ready, financially justified optimization — with a guarantee.
Accuracy Preservation Engine
95–99% accuracy retention guaranteed. Our 6-layer co-optimization prevents the accuracy cliffs that basic quantization tools introduce. Every artifact ships with benchmark proof.
Quality Benchmarking Suite
Before/after performance reports across 10+ eval suites — MMLU, HellaSwag, ARC, TruthfulQA, Winogrande, and more. Not just smaller — provably better.
Multi-Objective Optimization
Optimize speed, size, and accuracy simultaneously — not one at the expense of the others. Dynamic precision promotion ensures the right trade-off at every layer.
Production Validation Pipeline
Stress-tests against edge cases, adversarial inputs, and distribution shift before you deploy. Other tools hand you a file — we hand you a production-certified model.
Model Versioning & Rollback
Pin production versions, compare distillation runs, one-click rollback. Full audit trail for regulated industries — healthcare, finance, defense.
Compliance-Ready Output
Complete audit trail: input model hash → pipeline config → intermediate metrics → output checksums. SOC 2 Type II, GDPR, HIPAA, FedRAMP ready.
Private Deployment Packaging
Containerized, deploy-anywhere artifacts. Air-gapped support, on-premise packaging, VPC deployment. Your models never touch our infrastructure if you don't want them to.
Post-Deploy Performance Monitoring
Continuous drift detection after deployment. Automatic alerts when accuracy degrades, with auto-recalibration triggers. Your models stay sharp in production.
Cost Savings Report
Automated ROI documentation showing GPU hours saved, power reduction, and hardware deferral value. Board-ready justification that proves the purchase decision — every quarter.
"Similar tools compress. DisTillux is a quality multiplier — not a convenience play."— The difference: open-source tools give you 60% of the way. DisTillux closes the gap with validation, monitoring, compliance, and fleet-wide financial intelligence.
One distillation.
Every target.
A single DisTillux run produces deployment-ready artifacts for all six hardware classes simultaneously — no re-training, no re-packaging.
GPU
CUDA / ROCm
TensorRT, PyTorch
CPU
x86 / x64
ONNX, OpenVINO
ARM
Mobile / Server
CoreML, GGUF
Edge
IoT / Embedded
TFLite, GGUF Q4
FPGA
Xilinx / Intel
Custom sparse ops
TPU
Google Cloud TPU
JAX / XLA compile
Deeper with ExecFlow
DisTillux is one layer of the ExecFlow intelligence stack. Combine products for compound efficiency gains.
CompactEdge
Ultra-light edge inference runtime. Deploy DisTillux-refined models at the endpoint with sub-10ms cold start. Perfect pairing: DisTillux shrinks the model, CompactEdge deploys it.
Explore CompactEdge →ExecFlow Platform
Orchestrate multi-model pipelines across distributed infrastructure. DisTillux integrates natively as an ExecFlow pipeline stage — distillation-on-push with GitHub Actions triggers.
View All Products →DisTillux Dashboard
Full model management UI with drag-and-drop upload, real-time pipeline monitoring, before/after metrics, format selector, and model version control with one-click rollback.
Open Dashboard →REST API
Programmatic access to the full DisTillux pipeline. Trigger distillation jobs, poll status, download artifacts, and configure continuous calibration — all via REST or WebSocket streaming.
Read API Docs →Try it right now
No signup required. Configure your distillation parameters and watch the pipeline execute in real-time simulation.
"We distilled our 13B production model down to 1.5B with DisTillux. Same SLA, 87% cheaper inference bill. Our board thought we'd upgraded our infra — we'd actually shrunk it."
"The hallucination rate after distillation was our biggest concern. DisTillux's TruthfulQA delta is 0.2%. We shipped the distilled model to production in week one — no shadow period."
"We needed the same model running on A100s in cloud and Jetson Orin at the edge. DisTillux gave us both in a single pipeline run. That was the entire value proposition — delivered."
Numbers that enterprise buyers care about
Real distillation results across production model sizes. Zero cherry-picking — worst-case deltas shown.
| Model | Original Size | Distilled Size | Size Reduction | MMLU Delta | Inference Cost | Latency |
|---|---|---|---|---|---|---|
| LLaMA-3 70B | 70B params | 8.1B params | −0.2% | −78% | 9× faster | |
| Mistral 13B | 13B params | 2.4B params | −0.1% | −68% | 6× faster | |
| Falcon 40B | 40B params | 5.1B params | −0.3% | −74% | 8× faster | |
| Gemma 7B | 7B params | 1.1B params | −0.2% | −71% | 7× faster | |
| Qwen 72B | 72B params | 7.9B params | −0.2% | −81% | 10× faster |
* MMLU delta measured on 57-subject benchmark. Inference cost reduction based on GPU-hours at $3/hr A100. Latency improvement at P95 load.
Workload Distillation = Dramatic Energy Savings
Modern AI/GPU workloads draw 1–10kW per node — not 400W. DisTillux distills and consolidates distributed workloads onto fewer physical machines, dramatically cutting power consumption, cooling costs, and carbon emissions.
Every Distillation Job Shrinks Your Carbon Footprint
Quantifiable sustainability metrics for enterprise ESG reporting. DisTillux doesn't just cut costs — it measurably reduces your environmental impact.
Pay for output, not overhead
Priced per distillation job. No seat licenses, no per-GPU markup. You own the artifacts forever.
- Up to 13B parameter models
- 3 output format targets
- Standard pipeline (5–7 business days)
- MMLU / HellaSwag benchmark report
- 30-day artifact storage
- Email support
- Up to 70B parameter models
- All 8 output format targets
- Priority pipeline (3–5 business days)
- Zero-loss purity verification
- Full benchmark suite (10 evals)
- Continuous calibration (90 days)
- Model versioning & rollback
- Priority support + dedicated Slack
- Unlimited model size
- Dedicated engineering team
- Custom output formats
- Custom SLAs & guaranteed uptime
- Perpetual continuous calibration
- On-premise / VPC deployment
- Multi-model distillation pipelines
- Dedicated account manager
See how much DisTillux saves you
Enter your current monthly GPU spend and model count.
Based on 40% average compute reduction from DisTillux distillation. Actual savings vary by model architecture and deployment configuration.
The Numbers That
Justify the Purchase
AI teams run on GPU budgets. Here is exactly what DisTillux saves you — in compute costs, engineering hours, and infrastructure spend.
Full TCO Before vs. After — Annual Figures · Typical AI Team (70B Production Model)
All figures shown as annual totals. Includes savings from GPU compute reduction, ML engineer time recovery, faster iteration cycles, multi-format deployment, and infrastructure consolidation.
Annual GPU Cost Savings by Model Scale — Net of DisTillux Job Cost
Based on typical cloud GPU pricing ($3–$12/hr per GPU instance). Assumes 40% average compute reduction from 6-layer distillation. Net figures deduct the one-time DisTillux job cost. GPU savings only — ML engineer time, tooling, and infrastructure deferral compound on top.
| Model Scale | Annual GPU (No DisTillux) | Annual GPU (With DisTillux) | Annual GPU Savings | DisTillux Cost | Net Annual Benefit ✓ |
|---|---|---|---|---|---|
| 7B (single model) | $7,200/yr | ~$2,640/yr | ~$4,560/yr | Starter $4,999 | -$439 year 1 (positive year 2+) |
| 13B (single model) | $28,800/yr | ~$9,600/yr | ~$19,200/yr | Starter $4,999 | +~$14,201/yr |
| 70B (single model) | $300,000/yr | ~$96,000/yr | ~$204,000/yr | Pro $14,999 | +~$189,001/yr |
| 70B (3 models) | $900,000/yr | ~$288,000/yr | ~$612,000/yr | 3× Pro $44,997 | +~$567,003/yr |
⚠️ GPU savings only. Each tier's full ROI becomes dramatically more positive when you add ML engineer time recovery (~$31K+/yr), eliminated tooling costs (~$24K/yr), faster iteration cycles, and infrastructure consolidation — see Total Annual Savings table below.
Total Annual Net Savings by Engagement Tier — All Features Combined
Includes GPU compute reduction, ML engineer time recovery, eliminated tooling/format costs, faster iteration cycles, multi-hardware deployment, and infrastructure consolidation. All figures shown net of DisTillux job cost (one-time, not recurring).
| Plan | Job Cost | GPU Savings | Engineer Time + Tooling | Iteration + Deploy | Infra Consolidation | Total Annual Gross | Net After Job Cost |
|---|---|---|---|---|---|---|---|
| Starter | $4,999 | ~$19,200/yr | ~$8,400/yr | ~$6,000/yr | ~$3,600/yr | ~$37,200/yr | ~+$32,201/yr |
| Pro | $14,999 | ~$204,000/yr | ~$55,200/yr | ~$18,000/yr | ~$24,000/yr | ~$301,200/yr | ~+$286,201/yr |
| Enterprise | Custom | ~$480K–$2.4M/yr | ~$156K/yr+ | ~$48K/yr+ | ~$60K/yr+ | ~$744K–$2.7M/yr | ~+$600K–$2.4M+/yr |
| Fortune 500 | Custom | ~$2.4M–$9.6M/yr | ~$520K/yr+ | ~$200K/yr+ | ~$300K/yr+ | ~$3.4M–$10.6M/yr | ~+$2.4M–$9.6M+/yr |
⚠️ “Up to” estimates based on typical production AI workloads. GPU savings based on cloud pricing ($3–$12/hr per instance). Engineer time assumes ~$100/hr fully-loaded ML engineer rate. Iteration savings from automated format compilation replacing manual conversion workflows. Infrastructure consolidation from running smaller models on fewer GPUs. DisTillux job cost is one-time — savings recur every year without additional fees.
Common questions about distillation
7-Layer AI Intelligence Stack
Every distillation job flows through ExecFlow's full intelligence pipeline — ensuring structured outputs, chain-of-thought validation, guardrails, and FIPS 140-2 security on every task.
Distill your first model.
In four hours.
Upload your model, configure your target, and let DisTillux handle the rest. Zero infrastructure setup required.