The Fine-Tuning Spectrum (Oct 2025)

Full Fine-Tuning: Update all model parameters. Requires massive GPU memory (13B model = 26GB FP16 weights + 78GB gradients = 104GB total). Only viable for 8B models on single H200.

LoRA: Train tiny adapter layers (rank 8-64). 10× memory reduction. Llama 3.2 70B FP16: 140GB → 14GB trainable.

QLoRA: LoRA + 4-bit quantization. 20× memory reduction. Llama 3.2 70B: 140GB → 7GB trainable. Fits on single RTX 5090 (32GB).

GPU Memory Requirements (Llama 3.2 Models, Oct 2025)

Key Insight: QLoRA makes 70B fine-tuning accessible on consumer GPUs. A single RTX 5090 ($2,000) can fine-tune models that previously required $120K+ in datacenter GPUs.

Performance Trade-Offs

Training Speed (Llama 3.2 8B, ~10K samples, Oct 2025)

• Full FT: ~8 hours on H200 (1.5× faster than H100 due to HBM3e bandwidth)
• LoRA (rank 32): ~3 hours on H200 (3× faster - fewer params to update)
• QLoRA (4-bit): ~5 hours on RTX 5090 (2× faster than full FT, slower than LoRA due to dequantization)

Quality: Full FT vs LoRA vs QLoRA

When to Use Each Method

• Full Fine-Tuning: Only if 8B model + H200 budget + need absolute best quality
• LoRA: Production default. 1% quality loss, 10× memory savings, fast iteration
• QLoRA: Budget deployments, rapid experimentation, consumer GPUs (RTX 5090, 5080)

Production Deployment Guide

LoRA Fine-Tuning (Llama 3 8B on Single GPU)

# Install dependencies
pip install transformers peft datasets bitsandbytes accelerate

# Fine-tune with LoRA (16GB VRAM)
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.2-8B",
    load_in_8bit=True,
    device_map="auto"
)

lora_config = LoraConfig(
    r=32,  # rank (8-64 typical)
    lora_alpha=64,  # scaling factor (2× rank common)
    target_modules=["q_proj", "v_proj"],  # attention layers
    lora_dropout=0.1,
    bias="none"
)

model = get_peft_model(model, lora_config)
# Train with HuggingFace Trainer...
# Training time: ~3 hours on RTX 5090 (Oct 2025)

QLoRA Fine-Tuning (Llama 3.2 70B on RTX 5090)

# QLoRA with 4-bit quantization (32GB VRAM)
from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",  # NormalFloat 4-bit
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True  # nested quantization
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.2-70B",
    quantization_config=bnb_config,
    device_map="auto"
)

lora_config = LoraConfig(r=16, lora_alpha=32, ...)
model = get_peft_model(model, lora_config)
# Fits on single RTX 5090 (32GB) with headroom!

Serving Fine-Tuned LoRA Models

# Load base model + LoRA adapter
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-8B")
model = PeftModel.from_pretrained(base_model, "./lora-adapter")

# Or merge adapter into base for faster inference
model = model.merge_and_unload()
model.save_pretrained("./merged-model")

# Serve with vLLM (supports LoRA adapters natively)
python -m vllm.entrypoints.openai.api_server \
  --model ./merged-model \
  --tensor-parallel-size 1

Cost Analysis: Full FT vs LoRA vs QLoRA

Llama 3.2 70B Fine-Tuning (10K samples, ~6 hours training, Oct 2025)

Iteration Economics:

• Full FT: $108/iteration → 10 iterations = $1,080 (slow experimentation)
• LoRA: $7.20/iteration → 100 iterations = $720 (15× cheaper)
• QLoRA: $3.60/iteration → 200 iterations = $720 (30× cheaper, enables rapid tuning)