Perimattic optimizes AI models for production deployment — applying quantization, pruning, knowledge distillation, and inference optimization to reduce costs by up to 80% while maintaining accuracy. We profile, compress, and deploy; you pay less per request.
Optimization Runtimes & Tooling — TensorRT, ONNX Runtime, vLLM, Triton, GPTQ, OpenVINO, TFLite, CoreML, llama.cpp
AI model optimization is the process of making a trained machine learning or large language model faster, smaller, and more cost-effective to serve in production — without meaningfully degrading its accuracy. A model that achieves state-of-the-art benchmark scores in a research environment frequently costs five to fifty times more per request than necessary when naively deployed. The gap between research performance and production economics is where most AI projects stall.
The core techniques are quantization (reducing weight precision from FP32 to INT8 or INT4), pruning (removing parameters that contribute negligibly to outputs), knowledge distillation (training a compact specialist model from a large general-purpose one), and serving optimization (operator fusion, dynamic batching, and hardware-specific kernel tuning). Each technique targets a different constraint — memory, compute, or throughput — and the best outcomes come from combining them strategically.
For businesses running AI at scale, optimization is not a nice-to-have. A model serving ten million requests per day at $0.002 per request costs $20,000 daily. The same model, optimized to $0.0006 per request, saves $14,000 every day. At enterprise volumes, optimization ROI compounds faster than almost any other engineering investment.
Inference cost
High GPU spend that scales linearly with every request
Inference cost
Reduced 50–80% through quantization and distillation
Latency
500ms–5s per request on standard GPU hardware
Latency
Under 100ms on equivalent hardware after TensorRT / ONNX optimization
Memory footprint
Requires expensive A100-class GPU infrastructure
Memory footprint
Deployable on consumer GPUs, CPU servers, or edge devices
Edge compatibility
Incompatible with most edge hardware and mobile devices
Edge compatibility
ONNX and TFLite-ready for mobile, IoT, and embedded deployment
Redeployment cost
Manual rebuilds required for every hardware or framework change
Redeployment cost
Standardised ONNX format with CI/CD pipelines simplifies redeployment
The distinction matters most at scale. A model serving ten million daily requests at $0.002 per request costs $20,000 per day. Optimized to $0.0005 per request, the same model costs $5,000 — a saving that compounds faster than almost any other engineering investment.
Seven specialist service lines, each targeting a different constraint in your model's production deployment.
Reduce model size by 2–4× and inference latency by up to 4× through INT8 and INT4 quantization, with accuracy preserved within 0.5–2% of the original FP32 baseline. We apply post-training quantization (PTQ) and quantization-aware training (QAT) depending on your accuracy requirements.
Remove redundant parameters and attention heads from overparameterised models. Structured pruning removes entire neurons and layers for hardware-friendly sparsity; unstructured pruning maximises compression ratios. We benchmark accuracy at each pruning stage before proceeding.
Train a compact student model to match the behaviour of a large teacher model. Distilled models deliver 70–90% of teacher accuracy at 10–20% of the inference cost — purpose-built for your task rather than a compressed version of a general-purpose model.
Export models to the ONNX standard and apply TensorRT graph optimizations for NVIDIA hardware: operator fusion, kernel auto-tuning, and mixed precision inference. Typical results: 2–5× throughput gain and 60–70% latency reduction on GPU-served workloads.
Optimise models for deployment on Apple Silicon, ARM processors, edge TPUs, and embedded systems using CoreML, TensorFlow Lite, OpenVINO, and GGUF. We validate on your target hardware — not simulated environments — to guarantee latency and memory compliance.
Profile every layer of your model's forward pass to identify bottlenecks, operator fusion opportunities, and memory bandwidth constraints. We deliver profiling reports that specify exactly where time is spent and what each optimization technique will recover.
Implement dynamic batching, model caching, and request routing on Triton Inference Server or vLLM to maximise GPU utilisation and minimise per-request cost at production traffic volumes. We design the serving architecture alongside the model optimization so both are tuned together.
A structured six-stage process from free model audit and profiling through deployment and ongoing performance monitoring.
We profile your existing model end to end — measuring latency, throughput, memory usage, and accuracy — to establish a quantified baseline and identify the highest-impact optimization targets before we write a line of code. You leave with a clear picture of what to optimize and by how much.
We design your optimization plan: quantization approach (INT8, INT4, or FP16), pruning targets, distillation feasibility, runtime selection, and hardware targets. We set explicit accuracy thresholds before optimization begins so every trade-off is agreed upfront.
We apply the agreed optimization techniques iteratively, benchmarking against the baseline at each step. Quantization, structured pruning, and knowledge distillation runs are sequenced to maximize compression without crossing your accuracy thresholds.
We benchmark the optimized model against your task-specific evaluation sets, not generic leaderboards. We run regression tests against edge cases and compare outputs to the original model systematically before sign-off.
We export to ONNX, TensorRT, CoreML, or TFLite as required and integrate with your serving infrastructure — Triton Inference Server, vLLM, or a custom FastAPI endpoint. We include CI/CD pipelines for model versioning and rollback.
We instrument the deployed model with latency, throughput, and accuracy monitoring. We track for drift and provide a re-optimisation plan as model usage patterns evolve or new hardware targets emerge.
We profile your existing model end to end — measuring latency, throughput, memory usage, and accuracy — to establish a quantified baseline and identify the highest-impact optimization targets before we write a line of code. You leave with a clear picture of what to optimize and by how much.
We design your optimization plan: quantization approach (INT8, INT4, or FP16), pruning targets, distillation feasibility, runtime selection, and hardware targets. We set explicit accuracy thresholds before optimization begins so every trade-off is agreed upfront.
We apply the agreed optimization techniques iteratively, benchmarking against the baseline at each step. Quantization, structured pruning, and knowledge distillation runs are sequenced to maximize compression without crossing your accuracy thresholds.
We benchmark the optimized model against your task-specific evaluation sets, not generic leaderboards. We run regression tests against edge cases and compare outputs to the original model systematically before sign-off.
We export to ONNX, TensorRT, CoreML, or TFLite as required and integrate with your serving infrastructure — Triton Inference Server, vLLM, or a custom FastAPI endpoint. We include CI/CD pipelines for model versioning and rollback.
We instrument the deployed model with latency, throughput, and accuracy monitoring. We track for drift and provide a re-optimisation plan as model usage patterns evolve or new hardware targets emerge.
Select an industry to see how model optimization reduces inference cost and enables deployment at scale.
AI model optimization in financial services centres on real-time decision requirements — fraud detection, credit scoring, and trading signal models where milliseconds matter and inference cost scales with every transaction processed.
AI model optimization in financial services centres on real-time decision requirements — fraud detection, credit scoring, and trading signal models where milliseconds matter and inference cost scales with every transaction processed.
Healthcare AI optimization is driven by edge deployment requirements and data residency constraints — clinical models that must run on bedside devices, portable scanners, or within hospital networks without cloud dependency.
Retail AI optimization focuses on throughput at scale — recommendation and search ranking models that serve millions of daily requests where each millisecond of latency directly affects conversion and each GPU-dollar of inference cost affects margins.
Manufacturing AI optimization is driven by edge deployment on factory floors — quality inspection and predictive maintenance models that must run on embedded cameras, PLC controllers, and industrial edge computers without reliable cloud connectivity.
Legal AI optimization addresses the tension between document processing scale and data security — firms with strict data residency requirements need models that run entirely on-premise at an economically viable inference cost.
SaaS AI optimization focuses on serving economics — embedding, classification, and generation models that must meet strict latency SLAs for end-users while keeping per-API-call inference costs below the revenue model's unit economics.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade optimization engineering from shallow compression experiments.
We set measurable accuracy thresholds before optimization begins and benchmark against your specific task — not generic leaderboards. Every technique we apply is validated against your real evaluation data, and we do not ship an optimization that crosses your agreed threshold.
We optimize for your target hardware — NVIDIA A10 and A100, Apple Silicon, ARM CPUs, edge TPUs, and NVIDIA Jetson — not a theoretical average. Different hardware favors different optimization strategies: TensorRT for NVIDIA, OpenVINO for Intel, CoreML for Apple, TFLite for ARM.
We handle the full stack: profiling, compression, runtime selection, serving infrastructure, and CI/CD. You receive a deployed, monitored model — not a compressed weight file. We do not split optimization from deployment; both are tuned together.
Every engagement begins with a profiling baseline and ends with a benchmarked comparison. Latency, throughput, memory footprint, accuracy, and per-request inference cost are measured before and after so the ROI is concrete, not estimated.
“A production model that is three times faster and sixty per cent cheaper to serve is worth more than a benchmark model that cannot be deployed at scale — yet most teams skip optimization because they do not know where to start.”
AI model optimization is the process of making a trained machine learning or large language model faster, smaller, and more cost-effective to serve in production — without meaningfully degrading its accuracy. Core techniques include quantization (reducing numerical precision from FP32 to INT8 or INT4), pruning (removing redundant parameters and attention heads), knowledge distillation (training a compact student model from a larger teacher), and runtime optimization (ONNX export, TensorRT graph fusion, dynamic batching). The goal is to close the gap between research-quality model performance and economically viable production deployment.
These are three complementary but distinct compression strategies. Quantization reduces the number of bits used to represent each model weight — from 32-bit floats to 8-bit integers, for example — reducing memory and compute requirements by 2–4× with minimal accuracy loss. Pruning removes parameters whose contribution to the output is negligible: structured pruning removes entire neurons or attention heads (hardware-friendly), while unstructured pruning removes individual weights (higher compression, harder to accelerate). Knowledge distillation trains a smaller 'student' model to replicate the outputs of a larger 'teacher' model, producing a fundamentally smaller architecture rather than a compressed version of the original. Each technique targets different constraints; most production optimization engagements combine all three.
The range depends on the starting model and target hardware, but 50–80% cost reduction is typical for a well-executed optimization engagement. INT8 quantization alone commonly cuts per-request GPU time by 30–50%. Combining quantization with knowledge distillation — replacing a 70B parameter model with a distilled 7B model for your specific task — can reduce inference cost by 90% or more while preserving 95%+ of the original task accuracy. We benchmark before and after every engagement so the savings are quantified, not estimated.
Well-executed quantization preserves 95–99% of original accuracy for most tasks. INT8 quantization of transformer models typically produces accuracy loss of 0.5–2% on task-specific benchmarks, which is acceptable for the majority of production use cases. INT4 quantization introduces higher accuracy loss but remains viable for many generation tasks with careful calibration. We establish your minimum acceptable accuracy threshold before we begin and benchmark against it at every stage — if an optimization step crosses your threshold, we do not ship it.
We optimize across the major model families and frameworks. For large language models: GPT-4o, Llama 3 (8B and 70B), Mistral 7B and Mixtral, Gemma, Phi-3, and Falcon. For vision and multimodal models: ViT variants, CLIP, SAM, and YOLO families. For classical ML and tabular models: XGBoost, LightGBM, and scikit-learn pipelines. Frameworks covered include PyTorch, TensorFlow, JAX, and ONNX. We export to TensorRT, ONNX Runtime, OpenVINO, CoreML, TensorFlow Lite, and GGUF depending on your target deployment environment.
A focused quantization and benchmarking engagement for a well-defined model and deployment target typically takes two to four weeks. A complete engagement including profiling, quantization, pruning, distillation, evaluation, and deployment integration runs four to eight weeks. Projects involving large custom architectures, multiple target hardware environments, or complex accuracy requirements take longer. We provide a more specific timeline after the free model audit call.
Model optimization engagements typically cost $15,000 to $60,000 depending on the complexity of the model architecture, the number of optimization techniques applied, the depth of evaluation required, and the target deployment environment. Compute costs for quantization and distillation training runs are modest compared to pre-training and are included in our engagement estimates. We scope every project before quoting so there are no surprises.
Yes. Edge optimization is one of our primary specialisations. We convert models to TensorFlow Lite, CoreML, and ONNX Runtime for deployment on Android, iOS, and ARM-based hardware. For embedded and industrial edge systems — NVIDIA Jetson, Intel Neural Stick, and Raspberry Pi class devices — we use OpenVINO, TensorRT for Jetson, and GGUF for CPU-only inference. We validate on the actual target hardware rather than simulated environments, which is the only reliable way to confirm latency and memory compliance.
Yes. For sensitive workloads we run the entire optimization pipeline within your own cloud environment — AWS, Azure, or Google Cloud — or within your on-premise infrastructure. Quantization and distillation calibration runs require sample data, and we design the data handling process to comply with your data residency and privacy requirements from the start. We advise on any applicable regulatory constraints during the free scoping call.
We have delivered model optimization projects for clients in financial services (fraud detection, credit scoring, trading), healthcare and life sciences (clinical NLP, radiology, wearables), e-commerce and retail (recommendation, visual search, catalogue classification), manufacturing (quality inspection, predictive maintenance), legal and compliance (contract review, document extraction), and SaaS and technology (embedding APIs, code completion, multi-tenant inference). Our processes are designed to meet the data sensitivity and compliance requirements common in regulated sectors.
Tell us about your model and deployment target, and we will profile it, identify the highest-impact optimization techniques, and show you exactly what latency and cost reduction is achievable — before you commit to an engagement.