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Cost & Investment Guide

LLM Fine-Tuning & Quantization Cost Guide

Typical Investment Range: $20,000 - $75,000

Foundation model fine-tuning and quantization ranges from $20,000 for targeted LoRA adapter tuning to $75,000+ for full-parameter domain adaptation, AWQ quantization, and high-throughput vLLM serving.

Primary Variables

What Drives the Cost

Dataset Curation & Synthetic Generation

Raw text cleaning versus building 10,000 high-substance synthetic instruction Q&A pairs.

Fine-Tuning Methodology

Parameter-Efficient Fine-Tuning (PEFT/LoRA) versus full-parameter model weight training.

Model Size

8B parameter models (Llama 3 8B) versus 70B parameter models requiring multi-GPU H100 clusters.

Serving & Quantization

Standard FP16 inference versus AWQ/GPTQ 4-bit quantization tuned for sub-180ms time-to-first-token.

Worked Examples

3 Worked Examples at Different Scopes

Scope Tier Investment Range Timeline Included Specifications
Targeted Domain LoRA Adapter (Small) $20,000 - $32,000 3 - 4 Weeks LoRA fine-tuning on Llama 3 8B using 2,000 domain Q&A pairs, evaluated against MMLU benchmarks, deployed on single A10G GPU.
70B Parameter Model Fine-Tuning (Mid) $40,000 - $58,000 4 - 6 Weeks QLoRA fine-tuning on Llama 3 70B, synthetic instruction dataset expansion, custom evaluation harness, and AWQ 4-bit quantization.
High-Throughput vLLM Cluster Serving (Large) $65,000 - $75,000+ 6 - 8 Weeks Multi-GPU distributed fine-tuning, custom tokenizers, vLLM engine optimization with PagedAttention KV cache, and 480 tokens/sec streaming.
Scope Boundaries

Exclusions & Cost Escalators

What Is Excluded
  • Cloud GPU compute costs incurred during training (run on client AWS/RunPod GPU accounts).
  • Continuous weekly dataset re-training pipelines.
What Changes the Estimate (Escalators)
  • Training custom tokenizers for non-English specialized medical or legal terminology (+20%).
  • Multi-GPU distributed training across multi-node H100 clusters (+30%).
Calculate Custom Estimate

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Buyer FAQ

Frequently Asked Questions

Is fine-tuning better than RAG for enterprise data? ↓

Fine-tuning teaches a model style, tone, and domain vocabulary; RAG provides up-to-date factual retrieval. Most enterprise systems combine both.