RTX 5060 Ti 16GB for Synthetic Data Generation

Synthetic data generation is one of the most token-hungry workloads in modern NLP: a single instruction-tuning run burns 100M-1B tokens, and a classifier distillation dataset is often ten times larger. The RTX 5060 Ti 16GB on UK dedicated GPU hosting lets you run Llama 3.1 8B FP8 or Qwen 2.5 14B AWQ as a teacher model at fixed monthly cost – turning unbounded generation jobs into an overnight batch rather than a budget decision.

Contents

• Why self-host the teacher
• Generation throughput
• Task recipes
• Quality control
• Example YAML config

Why self-host the teacher

The economics flip around 500M tokens per month; above that, dedicated hardware wins outright and you also remove ToS restrictions on training with the outputs.

Generation throughput

With vLLM continuous batching, Llama 3.1 8B FP8 aggregates 720 tokens/second at batch 32, so a 500M-token dataset completes in roughly 190 wall-clock hours – about eight days continuous or two weeks at weekday-only operation. Qwen 2.5 14B AWQ trades half the throughput for stronger reasoning quality, which matters for hard instruction-following tasks.

Task recipes

• Instruction pairs – seed with topic plus persona, generate user turn then assistant turn with self-critique.
• Classifier training data – few-shot prompt per class with diversity constraints; hardest-negatives sampled from neighbouring classes.
• NER – generate a sentence plus inline span tags using JSON-schema guided output.
• RAG eval sets – given a document, produce answerable and unanswerable question pairs.
• Code-completion – Qwen Coder with docstring-to-implementation prompts.

Quality control

Pair the teacher with a BGE-base embedding deduplicator (10,200 texts/sec on the same card – see embedding throughput) and a BGE-reranker-base filter (3,200 pairs/sec) to drop near-duplicates and low-relevance outputs. Target 5-8% rejection rate; if it exceeds 20% your prompt is under-constrained.

Example YAML config

teacher:
  model: meta-llama/Meta-Llama-3.1-8B-Instruct
  quant: fp8
  backend: vllm
  batch: 32
  max_new_tokens: 512
  temperature: 0.8
  top_p: 0.95

task:
  type: instruction_pairs
  seed_file: seeds.jsonl
  target_count: 100000
  diversity_threshold: 0.82  # cosine distance

quality:
  dedup_embedder: BAAI/bge-base-en-v1.5
  reranker: BAAI/bge-reranker-base
  min_reranker_score: 0.55

See also: vLLM setup, FP8 Llama deployment, Qwen 14B benchmark, classification.