Services

Custom Models & Hosted Fine-Tuning

Ship tailored models, run managed distillation pipelines, or commission complete ML systems—powered end-to-end by the NexaCompute infrastructure.

What I Deliver

I help teams ship custom models and ML experiences without inheriting infrastructure overhead. Every engagement runs on NexaCompute—my reproducible, cost-aware ML lab—so you gain the outputs without worrying about the plumbing.

Custom Models

Bespoke language and multimodal models tuned to your domain.

  • Model scoping, evaluation harnesses, and deployment playbooks.
  • Domain-specific data curation with rigorous quality gates.
  • Inference-ready endpoints or artifacts you can host internally.

Hosted Fine-Tuning & Distillation

Managed fine-tuning pipelines that stay reproducible end-to-end.

  • Teacher-student distillation, LoRA/QLoRA, and PEFT strategies.
  • Telemetry, manifests, and cost tracking for every run.
  • Secure, provider-agnostic execution across Lambda, CoreWeave, RunPod, AWS, and more.

Custom Solutions & Tooling

Full-stack ML systems, dashboards, and automation tailored to your workflows.

  • Evaluation dashboards, Streamlit apps, and guardrail interfaces.
  • Batch or realtime inference pipelines with ops documentation.
  • Operator playbooks, handover sessions, and ongoing advisory.

Powered by NexaCompute

NexaCompute handles the data preparation, distillation, distributed training, evaluation, dashboards, and cost telemetry behind the scenes. You get durable artifacts and clear provenance while the compute layer remains disposable.

Want the full blueprint? Explore the NexaCompute architecture for module breakdowns, manifests, and pipeline details.

Case Studies

PROJECT CARD — NexaSci-Falcon-10B (Built on Nexa_Compute)

Role Scientific ML Engineer — Distillation, Infra, Deployment
Timeline 2–3 weeks (included building training infrastructure)
Cost ~$200
Infra Nexa_Compute (custom training + eval system)

Project Summary

I built a 10B-parameter scientific reasoning model — NexaSci-Falcon-10B — using my fully custom ML infrastructure (Nexa_Compute).

The model delivers near-frontier performance on scientific task structure (hypothesis generation + methodology design) while running on single-GPU inference hardware.

What I Did

  • Designed the data pipeline, generated/filtered 100k scientific Q&A
  • Distilled GPT-4/GPT-5-Mini outputs into Falcon-10B using QLoRA
  • Engineered strict dataset filters, semantic dedupe, and schema validation
  • Designed & executed a reproducible training pipeline
  • Built a rubric-based evaluation suite (correctness, grounding, clarity)
  • Benchmarking on RTX-4090s and A100s via vLLM
  • Documented everything with reproducible manifests + dashboards

Infrastructure (Nexa_Compute)

A full end-to-end ML system I wrote and maintained:

Data Engine

  • JSON → Parquet pipelines
  • Filtering, dedupe, semantic scoring
  • Dataset manifests + reproducibility hashes

Training Engine

  • QLoRA / GLORA pipelines
  • Distributed config templates (FSDP / DDP / PP)
  • tmux-plus-SSH isolated VM execution
  • Automated checkpointing + merges

Eval Engine

  • vLLM multi-model generation
  • GPT-4 judge scoring
  • Parquet-based evaluation logs
  • Streamlit dashboard visualizations

Inference Engine

  • Optimized vLLM presets (bf16 + batch sweeps)
  • INT4/AWQ quant readiness
  • Single-GPU scientific assistant deployment

Results

  • Within ~10% of GPT-4 / Claude on scientific reasoning tasks
  • Beats or ties several 30B–70B open-source models
  • Stable, structured, evidence-grounded outputs
  • Runs at ~878 tok/s on dual RTX-4090 (bf16)
  • Cost 3–6× less compute than comparable open-source models
  • Entire pipeline trained for ~$200
  • Fully reproducible using Nexa_Compute manifests
Note: The 2–3 week timeline included building the complete training infrastructure from scratch. With Nexa_Compute now established, similar projects can be delivered in significantly less time (typically 1–2 weeks) at the same or lower cost, as the infrastructure overhead is already handled.

This case study demonstrates the exact kind of scientific model I can build for clients — reliably, quickly, and cost-efficiently.

Pricing & Engagement Models

I offer flexible engagement models to match your project scope and timeline. All pricing is transparent and upfront—no surprises.

Hourly Rate

Use only for tiny scoped work

Hourly Consulting

$120/hr – $175/hr

Ideal for:

  • Small debugging tasks
  • Eval pipeline fixes
  • Inference optimization
  • Consulting calls
  • Data inspection

Note: I do not run entire LLM projects on hourly rates.

Fixed-Scope Project Pricing

Clients love knowing the price upfront. I love knowing my runway is protected.

Tier A — Small Project

$1,500 – $3,000
Duration: 1 week

Examples:

  • Fine-tune a model on your dataset
  • Clean + convert your data
  • Run evals or optimizations
  • Deploy model with vLLM

Tier B — Medium Project

$5,000 – $12,000
Duration: 2–4 weeks

Examples:

  • Full QLoRA distillation
  • Dataset creation (10–50k samples)
  • Evaluation suite + dashboard
  • Deployment on your infrastructure

The NexaSci-Falcon-10B case study fits this tier.

Tier C — Full Custom Scientific Model

$15,000 – $35,000
Duration: 1–2 months

Examples:

  • Domain-specific scientific LLM (bio, chem, materials, CFD)
  • Multi-stage distillation + post-training
  • Retrieval system + agentic layer
  • Reproducible pipeline + deployment

Significantly cheaper than competitors ($50k–$120k).

Scientific Model Quickstart Package

$3,000 Flat
Delivered in 1–2 weeks

Deliverables:

  • Dataset cleaning
  • 1 fine-tuned or distilled model
  • Evaluation report
  • Inference-ready export
  • Optional vLLM deployment config

Guaranteed result with no hassle—perfect for one-shot, production-ready solutions.

Monthly Retainer

Best for stability + long-term work. I recommend retainers for ongoing partnerships—they provide stability for both parties and ensure priority delivery.

Basic Retainer

$3,000/month

Includes:

  • 20 hrs of work
  • Priority responses
  • Bugfixing
  • Eval updates
  • Model maintenance

Pro Retainer

$6,000/month

Includes:

  • 40 hrs
  • Model distillation cycles
  • Dataset curation
  • Performance tuning
  • Infra assistance

Lab-Level Retainer

$10k–$15k/month

Includes:

  • Scientific model development
  • Retrieval + eval suite
  • Periodic training runs
  • Full Nexa_Compute integration
  • Tool-assisted agentic workflows

Ideal for biotech, materials, and research teams.

Why This Pricing Works: I delivered a near-frontier 10B model for $200 compute cost. My infrastructure is reproducible, and I can ship data + model + eval loops faster than 95% of the market. Scientific ML is a luxury skillset—clients pay for certainty and expertise, not just time.

Request a Build

Share a bit about your workload and I’ll follow up with an execution plan, timelines, and recommended infrastructure footprint.