Systems-first AI platform Pretraining, alignment, evaluation, and deployment in one control plane

Train domain-specific LLMs from first token to production endpoint.

Koddle AI provides the infrastructure layer for organizations that need to build custom language models from scratch, not just fine-tune a hosted API. Design data pipelines, run large-scale distributed training, benchmark emergent capabilities, and ship optimized inference stacks with full ownership of weights, datasets, and evaluation policy.

Request Invite Access Operator Login Inspect Training Stack

0 GPU training jobs orchestrated
0 TB/day ingest throughput
0 Cluster utilization target

$ koddle train launch \
  --run-id med-70b-v3 \
  --data-manifest s3://corpora/biomed/curated.yaml \
  --model-config configs/decoder/70b-moe.toml \
  --parallel tp=8,pp=4,dp=16 \
  --optimizer adamw-bf16 \
  --checkpoint-policy async-sharded \
  --eval-suite mmlu,hellaswag,gsm8k,truthfulqa

[scheduler] reserved 512 x H100 across 4 availability zones
[loader] deduplicated 12.4T tokens, retained 9.8T after quality filters
[trainer] global batch = 8.2M tokens | seq = 8192 | flops util = 58.7%
[safety] policy probes injected every 1,000 steps
[export] tensor + kv-cache quantization profile attached

Data Provenance

Distributed Systems

Model Safety

Inference Optimization

Evaluation Science

Platform Overview

Infrastructure for organizations building proprietary foundation models.

The platform is designed for teams that require repeatable training pipelines, measurable quality gates, and low-level control over model architecture, tokenization, optimization strategy, and serving topology.

Corpus Engineering

Build high-signal token corpora with ingestion, normalization, classifier-based filtering, semantic deduplication, contamination checks, and attribution-aware lineage tracking.

Training Orchestration

Launch fault-tolerant pretraining and continued training jobs across heterogeneous clusters with tensor, pipeline, sequence, and expert parallelism encoded as reproducible run graphs.

Evaluation Harness

Compare checkpoints against benchmark suites, domain task sets, synthetic stress tests, and regression probes with deterministic scoring and experiment metadata.

Alignment Pipeline

Run supervised finetuning, preference optimization, rejection sampling, and policy auditing with safety classifiers and deployment gating baked into the release path.

Training Stack

Technical layers exposed as an operator-friendly control plane.

1. Data Plane

Ingest web, code, enterprise, and scientific corpora into versioned manifests.

2. Compute Plane

Schedule multi-node training with topology-aware placement and elastic recovery.

3. Model Plane

Configure dense or MoE transformers, tokenizer regimes, and context scaling.

4. Eval Plane

Continuously score capability, factuality, latency, and safety before promotion.

Reference Configuration 70B Decoder / BF16

Architecture: Decoder-only Transformer, grouped-query attention, rotary position encoding
Context Window: 8k native, 32k extrapolated with long-context curriculum
Parallelism: TP 8 / PP 4 / DP 16 with ZeRO-style optimizer partitioning
Data Pipeline: Rule filters, classifier gating, MinHash dedup, PII scrubbing, provenance logs
Serving Path: Sharded inference, paged KV cache, speculative decoding, quantized export

Operator Workflows

Move from corpus acquisition to reliable production deployment.

Pretraining

Construct multi-stage curricula, tune optimizer schedules, and checkpoint every phase with resumable artifact lineage.

Tokenizer training and vocabulary diagnostics
Streaming dataloaders with shard balancing
Loss, gradient, and throughput observability

Post-Training

Adapt foundation checkpoints into production assistants using supervised and preference-driven refinement strategies.

Instruction tuning and preference datasets
Reward modeling and policy optimization loops
Safety probes and refusal policy calibration

Serving

Promote validated checkpoints into low-latency inference profiles tailored to throughput, memory, and compliance requirements.

KV-cache aware batching
INT8 / FP8 deployment tracks
Latency and quality regression gates

Benchmark Surface

Promotion decisions based on measurable capability, not intuition.

Suite Target Use Case

MMLU 84%+ General reasoning and domain breadth

GSM8K 76%+ Multi-step arithmetic reliability

HumanEval 58%+ Code synthesis correctness

TruthfulQA 61%+ Factuality and reduced hallucination rate

Internal Domain Set P95 SLA Organization-specific accuracy threshold

eval/promotion.policy

release_if:
  capability_delta >= +1.8
  hallucination_rate <= previous - 12%
  harmful_content_refusal >= 0.97
  p95_latency_ms <= 185
  cost_per_1m_tokens <= target_budget

rollback_if:
  drift_alert == true
  benchmark_regression_count > 1
  safety_gate == failed

Engagement Model

Work with Koddle AI on model architecture, training strategy, and productionization.

Whether you are building a vertical foundation model, sovereign LLM stack, or enterprise-grade reasoning engine, Koddle AI provides the systems layer to own the full training lifecycle.

Request Platform Invite