Benchmark Overview
LoRA (Low-Rank Adaptation) fine-tuning is the standard method for adapting LLMs to specific tasks without full parameter updates. We benchmarked LoRA and QLoRA training speed across GPU models to quantify how hardware selection affects training time and cost on dedicated GPU hosting.
Test Configuration
Framework: PyTorch with Hugging Face PEFT. Models: Llama 3 8B, Llama 3 70B. LoRA config: rank 16, alpha 32, target modules (q_proj, k_proj, v_proj, o_proj). QLoRA: 4-bit NF4 base model, FP16 LoRA weights. Dataset: 10,000 instruction-response pairs, max 1,024 tokens. Training: 3 epochs, batch size 4 (gradient accumulation to effective batch 16).
LoRA Fine-Tuning Speed (Llama 3 8B)
| GPU | VRAM Used | Throughput (samples/s) | Time (3 epochs, 10K samples) | Cost Estimate |
|---|---|---|---|---|
| RTX 5090 (24GB) | 22 GB (QLoRA) | 3.8/s | 2.2 hours | Low |
| RTX 6000 Pro (48GB) | 18 GB (LoRA FP16) | 3.2/s | 2.6 hours | Medium |
| RTX 6000 Pro 96 GB | 18 GB (LoRA FP16) | 5.5/s | 1.5 hours | Medium |
| RTX 6000 Pro 96 GB | 18 GB (LoRA FP16) | 8.2/s | 1.0 hours | Higher |
QLoRA Fine-Tuning Speed (Llama 3 70B)
| GPU | VRAM Used | Throughput (samples/s) | Time (3 epochs, 10K samples) |
|---|---|---|---|
| RTX 5090 (24GB) | Cannot fit | – | – |
| RTX 6000 Pro (48GB) | 42 GB | 0.8/s | 10.4 hours |
| RTX 6000 Pro 96 GB | 42 GB | 1.4/s | 6.0 hours |
| RTX 6000 Pro 96 GB | 42 GB | 2.1/s | 4.0 hours |
| 2x RTX 6000 Pro 96 GB | 42 GB total | 2.5/s | 3.3 hours |
Memory Analysis
QLoRA enables 70B fine-tuning on a single 48GB GPU by keeping the base model in 4-bit precision. Full LoRA on 70B requires 140GB+ VRAM (2x RTX 6000 Pro minimum). For 8B models, both LoRA and QLoRA fit on 24GB GPUs, but LoRA at FP16 on larger GPUs trains 10-15% faster due to fewer dequantisation operations. Check GPU recommendations for VRAM requirements.
Multi-GPU Scaling
LoRA fine-tuning scales sub-linearly with multiple GPUs. Two RTX 6000 Pros provide approximately 1.8x speedup over one RTX 6000 Pro for 70B QLoRA (not 2x due to gradient synchronisation overhead). For 8B models, multi-GPU adds unnecessary complexity since single-GPU training completes in 1-2 hours. Use multi-GPU clusters only for 70B+ model training where single-GPU time exceeds your budget.
Recommendations
For 8B model LoRA: RTX 5090 with QLoRA delivers the best cost-performance, finishing in 2 hours. For 70B QLoRA: RTX 6000 Pro 96 GB balances speed and cost at 6 hours per run. RTX 6000 Pro is justified for teams running multiple fine-tuning experiments daily. Deploy training workloads on GigaGPU dedicated servers with private hosting for data security. Serve fine-tuned models via vLLM. See the benchmarks section, infrastructure blog, and LLM hosting for deployment patterns.
