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Melt data.
Forge intelligence.

Bytemelt is an R&D platform for training AI models on your own data. Compute power included.

Book demo callHow it works

You bring data. The platform trains the models.

Bytemelt's own models label your data, then training and testing run automatically; no one at Bytemelt opens it.
Who does what. Your team: choose the data, set the label classes, correct a sample of labels, set test margins, decide what ships. Bytemelt: GPU servers, shared storage, its own labelling models, training and testing, monitoring and support. Both work on one copy of your data, opened only by your team's accounts.

How it works

Raw files in. Tested releases out.

Six stages on one copy of your data: ingest, mine, label, train, adapt and validate.
Pipeline: five raw files (a camera frame, call audio, a lidar sweep, a claim form and a sensor log) flow through six stages, 01 Ingest, 02 Mine, 03 Label (melt data) and 04 Train, 05 Adapt, 06 Validate (forge intelligence), into a model card: base-model release candidate v14, scored against release v13, 3 of 4 test sets within margin, blocked by 1 test set beyond its margin.

Melt dataPrepare the data · 01–03

  1. 01Ingest

    Load raw files, drop corrupt ones, catalogue the rest

  2. 02Mine

    Find hard cases; drop near-duplicate files

  3. 03Label

    Auto-label; your reviewers correct a sample

Forge intelligenceTrain and test the model · 04–06

  1. 04Train

    Retrain the base model when new data lands

  2. 05Adapt

    Fine-tune per site, market, language or customer

  3. 06Validate

    Optimise for target hardware, then test

Review queue for batch 0412: a synthetic street frame with auto-labelled boxes (car 0.97, car 0.94, pedestrian 0.91, truck 0.88, a cyclist at 0.62 sent to review) and an anonymised face; 48,120 files auto-labelled, 962 low-confidence labels to review, 827 reviewed, 131 corrected.

Stages 01–03

Data processing and auto-labelling

Built-in labelling models draw boxes, masks, classes, trajectories and transcripts.

  • Rank files by likely model error
  • Send low-confidence labels to your reviewers
Run v15, a full retrain on 8 GPUs at step 84,000 of 120,000: validation-loss curves for v13, v14 and v15, dataset v41 of 41.2 TB, validation accuracy 0.731, 9 h 40 min elapsed, 4 h 09 min remaining, GPU utilisation 94%.

Stage 04

Model training

Every release traces back to the exact dataset version it saw.

  • Compare each run with the last release
  • Watch loss, accuracy and GPU use live
One perception base model, release v13, adapted three ways: site B in snow (+3.6 mAP), site C in rain (+2.9 mAP) and camera rig v2 (+1.7 mAP).

Stage 05

Model adaptation

Retune each adapted model when conditions change: a new site, camera rig, accent or form layout.

Closed-loop simulation at a synthetic junction: episode 3,214 of 5,000, minimum distance to collision 2.1 m against 1.8 m for release v13, 3,190 passed and 24 failed.

Stage 06

Simulation and validation

Run closed-loop simulation: the model drives a simulated machine and sees the result of each action.

Research leaderboard: abl-07 with radar added leads at 0.742 validation accuracy, then a world-model pretrain at 0.739, a larger backbone at 0.731, the baseline at 0.718 and a run without lidar at 0.684.

All stages

Applied AI research

Your researchers run architecture tests, world-model experiments and ablation studies on the same data and compute.

Industries

Train models for four industries

Synthetic lidar sweep from above: the ego vehicle, tracked cars, a cyclist and people, and the planned path.

Machines that move

Train perception, planning and end-to-end models on camera, lidar and radar logs.

  • Autonomous driving
  • Robots and civil drones
  • Trucks and logistics yards
  • Mines and ports
See machines that move
A public-domain chest X-ray with one auto-labelled nodule (0.87) confirmed by a specialist.

Healthcare

Train models on patient data that stays under your control.

  • Medical imaging
  • Surgical video
  • Patient monitoring
  • Clinical notes
See healthcare
A contact-centre transcript switching between Arabic and English, with agent and customer turns under a waveform.

Banking and insurance

Train models on customer calls, claims and contracts, and tune them for each language and product.

  • Contact-centre speech
  • Claims
  • Contracts and KYC
See banking and insurance
A building site seen from a drone (public-domain photo) with a BIM wireframe overlay and one clash marked: duct × beam, level 3, grid C-4.

Architecture and construction

Train models on drawings, BIM models, site cameras and drone flights.

  • Design checking
  • Site safety
  • Progress tracking
  • Quality inspection
  • Machinery
See architecture and construction

Another industry?

If your models learn from images, video, audio, sensor logs, documents or simulation output, you can train them on Bytemelt.

Book demo call

Quality

Regressions stop at the gate

Every release candidate runs the last release's test sets; a drop beyond your margin blocks it until you sign off.

Your team sets the metric and margin for each test set, such as accuracy, false-positive rate, word error rate or distance to collision in simulation. Each result carries its 95% confidence interval.

release v13 → release candidate v14

  • Night pedestrians, detection recall

    Cases
    2,140
    Last release
    0.94
    Release candidate
    0.95
    Margin
    −0.02
    Result
    Pass

  • Dust and haze, detection recall

    Cases
    860
    Last release
    0.81
    Release candidate
    0.80
    Margin
    −0.02
    Result
    Within margin

  • Occluded cyclists, detection recall

    Cases
    1,920
    Last release
    0.90
    Release candidate
    0.86
    Margin
    −0.02
    Result
    Fail −0.04

  • Simulation, min distance to collision

    Cases
    5,000 episodes
    Last release
    1.8 m
    Release candidate
    2.1 m
    Margin
    −0.2 m
    Result
    Pass

Release candidate blocked: 1 test set beyond its margin

Interface preview · sample data

Data control

Your data and weights stay yours

Seven answers for your security and compliance review.

Before you upload health or bank records, we confirm with your compliance team which data the contracted site may process.

Data path: your data → ingest, anonymised with your rules → your workspace, opened only by your team's accounts → export of data, labels and weights. An access log lists each read and export by a reviewer or a training job; your team reads the full log.
Ownership
Your data, labels and trained weights belong to you under the contract.
Access
Bytemelt has no access to your data or weights: your contract says so, and the platform is built so only accounts your team controls can open them.
Anonymisation
Your team anonymises faces, plates and names at ingest with the platform's tools, using rules you set, or before the files leave your own systems.
Audit log
Every access to your data, by a person or a job, is logged, and your team reads the log.
Location
Your data is stored and processed only at the partner data centre your contract names, in the Middle East and outside the EU. The contract names the site and country before any data moves, and data leaves the site only when your team moves it. Personal data from the EU moves only under transfer safeguards such as Standard Contractual Clauses, and we give your data-protection officer the site details a transfer impact assessment needs.
Vendor review
Before you sign, we answer your vendor-risk questionnaire, and your contract names the data-centre operator and the data-protection terms.
Export
Download your data, labels and trained weights whenever you want.

Compute

Designed for NVIDIA HGX B300

Eight Blackwell Ultra GPUs and 2.1 TB of GPU memory per server, joined by NVLink.

Vision, language and world models need large GPU memory and fast links between GPUs. A model whose weights, gradients and optimiser state fit in one server's memory trains without splitting across the network. The platform is designed for larger runs across several servers, all reading the same shared storage.

Illustration of the NVIDIA HGX B300 board layout: eight B300 GPU modules around the NVLink Switch chips.
Blackwell Ultra GPUs per HGX B300 server
8
total GPU memory
2.1 TB
total NVLink bandwidth
14.4 TB/s
dense FP8 for training
36 PFLOPS

Shared storage

Every stage reads one shared store, so no tool keeps its own copy.

Flat subscription, not per GPU-hour

Retraining every night costs the same as retraining once a month.

Specifications per 8-GPU HGX B300 server, nvidia.com/en-us/data-center/hgx, read 09.11.2026. Dense FP8 is half of NVIDIA's 72 PFLOPS sparse figure.

Questions about Bytemelt

What is Bytemelt?

Bytemelt is an R&D platform for training AI models on your own data, with compute power and storage included. Bytemelt's own models label your raw data, and the platform trains and tests your models automatically. Bytemelt is based in Abu Dhabi, UAE.

How is this different from renting GPUs?

Rented GPUs are hardware, usually billed by the hour, and you build and staff the pipeline. Bytemelt includes compute power, storage and the pipeline from raw data to a tested release in one subscription, and has no access to your data.

How is it priced?

By subscription, never per GPU-hour: one price covers compute power, storage and pipeline tools. Your contract names the capacity, term and price; we quote after a one-hour call. More capacity for a release is a contract change.

Who owns the data and the models?

You do. Your data, labels and trained weights are yours under the contract. Bytemelt has no access to them: your contract says so, and the platform is built so only accounts your team controls can open them. Bytemelt licenses its platform to you.

Where is my data stored and processed?

Only at the partner data centre your contract names, in the Middle East and outside the EU. The contract names the site and country before any data moves, and data leaves the site only when your team moves it. See Your data for EU transfers.

Do we need our own ML team?

Not to label, train or test: Bytemelt's own models label your data, and the platform trains and tests automatically. Your team sets the goals and margins and decides what ships. Bytemelt sells the platform, not model training to order.

What runs on the platform?

Five workloads: data processing and auto-labelling, model training, model adaptation, simulation and validation, and applied AI research. Labelling runs on Bytemelt's own models; training and testing run automatically.

Who is Bytemelt for?

Companies that train AI models on their own images, video, audio, sensor logs or documents. Four industries have their own pages: machines that move, healthcare, banking and insurance, and architecture and construction.

Who can use Bytemelt?

Companies training AI models for civil use. Advanced NVIDIA GPUs fall under export controls, so we check each company's ownership and end use before its account opens.

When can we start?

Accounts open when your subscription starts. On a one-hour call we size the compute and storage your models need; your contract then names your start date and capacity.

Where is Bytemelt based?

Abu Dhabi Global Market (ADGM), Abu Dhabi, United Arab Emirates. Bytemelt is built for companies in Europe, the Middle East, Africa and Southeast Asia.

Cost

What your own training platform costs

One HGX B300 server with storage, tools and engineers costs an estimated USD 0.7–2.8 million a year.

  • GPU rent, one HGX B300 server

    Low
    USD 403,000
    High
    USD 666,000
    Basis
    Market medians, reserved and on demand

  • NVIDIA AI Enterprise, 8 GPUs

    Low
    USD 0 (open-source tools instead)
    High
    USD 36,000
    Basis
    NVIDIA list price

  • Experiment tracking

    Low
    USD 3,600 (self-hosted MLflow infrastructure)
    High
    USD 107,000 (Weights & Biases, top of range; median USD 47,625)
    Basis
    Weights & Biases: buyer-reported prices; MLflow: published infrastructure estimate

  • Storage, curation and labelling tools, evaluation, security

    Low
    USD 95,000
    High
    USD 736,000
    Basis
    Bytemelt estimate

  • Platform engineers, 2 to 5

    Low
    USD 240,000
    High
    USD 1,250,000
    Basis
    Bytemelt estimate

  • Total a year

    Low
    About USD 0.7 million
    High
    About USD 2.8 million
    Basis
    Bytemelt estimate, November 2026

Market priceBytemelt estimate

Bytemelt

Compute power, storage and pipeline tools in one subscription, quoted after a one-hour call. Your engineers start on the data, not the cluster.

Sources: getdeploying B300 price tracker, read 09.11.2026; NVIDIA AI Enterprise licensing guide, USD 4,500 per GPU-year; Vendr data on 65 Weights & Biases Enterprise purchases, relayed by Spheron, and Spheron's self-hosted MLflow cost estimate, 2026. GPU rent: 8 GPUs × 8,760 hours at the reserved median (USD 5.75 per GPU-hour) or the on-demand median (USD 9.50). Bytemelt estimates are assumptions, November 2026. Market prices for the alternative, not Bytemelt prices.

Have data to melt?

In one hour, the founder sizes the compute your models need.

Book demo callEmail us

contact@bytemelt.ai

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