Unlocking Performance: Mastering LLMs on Limited Hardware#

What Are Large Language Models?#

Large Language Models are deep learning models—often based on Transformer architectures—that excel at understanding and generating text. The hallmark of these models is their large number of parameters (often in the billions). These models are typically trained on massive text corpora to develop a comprehensive understanding of language patterns.

Examples:

• GPT-3 family
• LLaMA
• ChatGPT (an iteration of GPT-based models refined by OpenAI)
• BERT-based models (though some consider them smaller in parameter count relative to GPT-3-sized models)

Why Do They Require So Many Resources?#

1. High Parameter Count: Hundreds of millions or billions of parameters translate to extensive memory requirements.
2. High Computational Demand: Training or even inference can require significant GPU hours due to the intense floating-point operations.
3. Memory Bandwidth: Moving large amounts of data (model weights and intermediate activations) between GPU and CPU or between GPU cores demands high bandwidth.

What Constitutes “Limited Hardware”?#

• CPU-Only Workstations: Machines with no dedicated GPU, relying solely on CPUs like Intel Xeon, AMD Ryzen, or Apple Silicon.
• Small or Older GPUs: Consumer-grade GPUs (e.g., NVIDIA GTX 1060 with 6 GB VRAM) or older professional GPUs that lack the memory footprint of newer data-center GPUs.
• Low RAM: Systems that might have enough GPU memory but minimal system RAM, limiting how large a model you can load.
• Edge Devices: Embedded systems with constrained power and compute resources (e.g., Jetson Nano, Raspberry Pi, or specialized boards).

Why Try to Run LLMs on Limited Hardware?#

1. Cost-Efficiency: Not everyone can afford expensive GPUs or pay for large cloud instances.
2. Democratization of AI: A broader community can tinker, innovate, and deploy specialized solutions.
3. Privacy and Control: Running models locally can ensure data privacy and reduce dependency on cloud services.
4. Experimental or Niche Use Cases: For specialized tasks or prototyping, you may not need the overhead of a full HPC cluster.

Choosing a Framework#

While there are multiple frameworks available—such as TensorFlow, PyTorch, JAX, and others—PyTorch has widely become the de facto standard in the NLP community, thanks to:

• Extensive support for advanced features.
• A large ecosystem of tutorials and open-source repositories.
• Hugging Face Transformers integration.

That said, TensorFlow and JAX also have robust capabilities and specialized optimizations, but many of the techniques described in this post are framework-agnostic.

Python Environment#

Having a clean Python environment can save a lot of headaches:

1. Use Virtual Environments: Conda or venv to isolate dependencies.
2. Install PyTorch: Make sure to install the CPU or GPU-enabled version matching your hardware.
3. Install Hugging Face Transformers: Provides easy access to a wide range of checkpointed models.
4. Install Additional Libraries: For example, numpy, scipy, scikit-learn (for data manipulation), and any specialized libraries for quantization (like bitsandbytes).

Example commands for Conda:

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conda create -n llm-limited-env python=3.9
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conda activate llm-limited-env
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pip install torch torchvision torchaudio
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pip install transformers
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pip install numpy bitsandbytes

GPU or CPU Drivers#

• NVIDIA GPU: Ensure you have the correct driver and CUDA toolkit for your GPU.
• AMD GPU: PyTorch has been expanding support for ROCm. Check version compatibility.
• Apple Silicon (M1/M2): Official support is available in recent PyTorch versions.

Types of Quantization#

1. Float16 (FP16): Significantly reduces memory usage and training/inference time compared to FP32.
2. Binary or Ternary Quantization (Experimental): Weights are restricted to {-1, +1} or {-1, 0, +1}, drastically reducing memory but with non-trivial accuracy losses.
3. Integer Quantization (8-bit, 4-bit): Maps weights into an 8-bit (or lower) integer range. This can dramatically cut down memory footprint.

Trade-Offs#

• Model Accuracy: 8-bit or 4-bit quantization may lead to slightly lower accuracy or require fine-tuning post-quantization to regain performance.
• Speed vs. Accuracy: Lower precision typically increases speed and decreases memory usage but can degrade performance on tasks unless carefully calibrated or specialized hardware instructions are used.

Tools and Libraries for Quantization#

• Hugging Face Transformers (with bitsandbytes): Offers 8-bit and 4-bit quantization out of the box for many Transformer-based LLMs.
• TensorRT / ONNX Runtime: NVIDIA’s TensorRT or the ONNX Runtime can perform quantized inference with GPUs that support mixed-precision.
• Intel Neural Compressor: Provides CPU-based quantization support for TensorFlow and PyTorch models.

Below is an example snippet demonstrating how to load a 4-bit quantized model via Hugging Face Transformers:

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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "facebook/opt-1.3b"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Using bitsandbytes to load the model in 4-bit precision
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model = AutoModelForCausalLM.from_pretrained(
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    model_name,
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    load_in_4bit=True,
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    device_map="auto"
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)
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input_text = "Hello, how are you?"
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inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
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with torch.no_grad():
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    outputs = model.generate(**inputs, max_length=50)
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print(tokenizer.decode(outputs[0]))

Batching#

• Inference Batching: Process multiple input sequences together for better GPU utilization.
• Training Batches: Too large a batch can exceed memory, while too small a batch can degrade throughput. A dynamic or gradient accumulation approach may help.

Caching#

• Tokenizer Caching: Tokenization is often CPU-bound. Pre-tokenize your dataset and store the tokens to speed up subsequent runs.
• Output Cache: For repeated queries or partial reuse (like in conversational agents), caching intermediate activations can reduce computations.

Efficient Sampling#

When running inference or generating text:

• Beam Search vs. Greedy: Beam search can be more expensive. For limited hardware, you might use smaller beam sizes or simpler sampling strategies (top-k, nucleus sampling).
• Chunking: Instead of processing very long sequences at once, break them into manageable chunks, especially if you’re dealing with GPT-like models that can handle incremental states.

CPU Offloading#

• Pipeline Parallelism: Split the model architecture so that certain layers run on the CPU while others run on the GPU.
• Layer-by-Layer Offload: Offload layers to CPU memory when they are not in use.

When to Use CPU-Only Inference#

• Cost Constraints: No suitable GPU or minimal GPU memory.
• Batch Size is Very Small: For a single input at a time, a CPU might suffice, though it will be slower.
• Specialized CPU Features: Modern Intel CPUs with AVX-512 or DL Boost instructions can offer surprising performance for quantized inference.

Example CPU-only code with a quantized model:

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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "EleutherAI/gpt-neo-1.3B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Load CPU-only, quantized in 8-bit
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model = AutoModelForCausalLM.from_pretrained(
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    model_name,
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    torch_dtype=torch.float16,
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    device_map={"": "cpu"}
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)
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model.eval()
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input_text = "What is the capital of France?"
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inputs = tokenizer(input_text, return_tensors="pt")
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with torch.no_grad():
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    outputs = model.generate(**inputs, max_length=30)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))

While the above snippet uses float16, you could combine it with an 8-bit approach using libraries like bitsandbytes if you’re on a platform that supports that.

Mixed Precision Training and Inference#

Use half-precision (FP16) or TF32 (on newer NVIDIA GPUs) to speed up calculations. In PyTorch, you can often wrap training loops with torch.cuda.amp.autocast() to automatically use mixed precision where beneficial.

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for batch in dataloader:
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    with torch.cuda.amp.autocast():
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        outputs = model(**batch)
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    loss = outputs.loss
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    scaler.scale(loss).backward()
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    scaler.step(optimizer)
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    scaler.update()
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    optimizer.zero_grad()

Gradual Layer Freezing#

When fine-tuning on limited hardware, you might not need to update every parameter in the model. A common strategy is to:

1. Freeze early layers.
2. Only fine-tune the last few layers or a specialized adaptation head (LoRA, adapters).

This reduces VRAM usage (fewer gradients to store/compute) and can still yield meaningful performance gains on specific tasks.

Model Partitioning#

Split large models across multiple GPUs if you have them (even if each GPU has modest VRAM). See the section on Model Parallelism for more detail.

Model Parallelism#

The model’s layers or sub-blocks are split across multiple GPUs or nodes:

• Pipeline Parallelism: Different GPUs hold different layers, processing mini-batches sequentially in a pipeline.
• Tensor Parallelism: Splits weights and operations across multiple GPUs at the tensor level.

Sharded Gradients#

Libraries like DeepSpeed or FairScale offer “Zero Redundancy Optimizer (ZeRO)” techniques, which shard both model states and gradients across multiple devices:

• ZeRO Stage 1: Optimizer states are sharded.
• ZeRO Stage 2: Gradients are sharded as well, further reducing memory overhead.
• ZeRO Stage 3: Model weights themselves are sharded.

This approach is complex but can allow training models with hundreds of billions of parameters across multiple GPUs, each with modest VRAM.

A typical approach might look like:

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import deepspeed
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b")
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ds_config = {
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    "train_batch_size": 1,
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    "gradient_accumulation_steps": 8,
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    "fp16": {
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        "enabled": True
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    },
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    "zero_optimization": {
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        "stage": 3
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    }
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}
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model_engine, optimizer, _, _ = deepspeed.initialize(
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    model=model,
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    model_parameters=model.parameters(),
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    config=ds_config
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)

Be prepared for a more complex setup, though—the returns can be significant if you need massive scale.

9.1 Transfer Learning and Fine-Tuning#

You rarely need to train an LLM from scratch. Pretrained models can be fine-tuned on specific tasks with significantly less computational overhead. Strategies include:

• Full Fine-Tuning: Adjust all model parameters, usually feasible with smaller models or specialized parallel strategies.
• Parameter-Efficient Fine-Tuning: Techniques like LoRA (Low-Rank Adaptation), adapters, or prefix tuning can dramatically reduce resource usage by only adding and training a small fraction of the total parameters.

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from peft import LoraConfig, get_peft_model
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from transformers import AutoModelForCausalLM
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lora_config = LoraConfig(
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    r=8,
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    lora_alpha=32,
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    target_modules=["q_proj","v_proj"],
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    lora_dropout=0.1
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)
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model = AutoModelForCausalLM.from_pretrained("facebook/opt-1.3b")
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model = get_peft_model(model, lora_config)
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# Train as usual
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for batch in dataloader:
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    outputs = model(**batch)
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    loss = outputs.loss
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    loss.backward()
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    optimizer.step()
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    optimizer.zero_grad()

9.2 Gradient Accumulation#

If your GPU can’t handle the batch size you desire, you can accumulate gradients across multiple forward passes before performing a backward pass. This simulates a larger batch at the cost of slightly slower throughput but much smaller peak memory usage.

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gradient_accumulation_steps = 8
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optimizer.zero_grad()
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for step, batch in enumerate(dataloader):
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    outputs = model(**batch)
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    loss = outputs.loss
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    loss = loss / gradient_accumulation_steps
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    loss.backward()
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    if (step + 1) % gradient_accumulation_steps == 0:
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        optimizer.step()
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        optimizer.zero_grad()

9.3 Checkpointing and Activation Offloading#

• Activation Checkpointing: Saves memory by re-computing intermediate activations on-the-fly during backward pass.
• Offloading Activations: Move rarely accessed activations to CPU memory (or even disk), though this can slow down training.

10.1 Low-Memory Inference on CPU#

Assume you have a machine with 16 GB of system RAM and no dedicated GPU. You want to run a GPT-2-like model for text generation.

1. Setting up the environment:

   • PyTorch CPU version installed.
   • Transformers library installed.

2. Loading a quantized model:

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import torch
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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model_name = "gpt2-medium"  # ~345M parameters
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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# Using dynamic quantization
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model = GPT2LMHeadModel.from_pretrained(model_name)
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model = torch.quantization.quantize_dynamic(
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    model, {torch.nn.Linear}, dtype=torch.qint8
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)
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model.eval()
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# Now model size in memory can be significantly smaller

3. Generating text:

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input_text = "In a future world,"
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input_ids = tokenizer.encode(input_text, return_tensors="pt")
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with torch.no_grad():
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    output_ids = model.generate(
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        input_ids,
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        max_length=50,
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        do_sample=True,
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        top_k=50,
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        top_p=0.95
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    )
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print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

You might observe some slowdown on CPU, but it’s now feasible within a limited memory footprint due to quantization.

10.2 Smaller GPU Training with Gradient Accumulation#

Suppose your GPU has 8 GB of VRAM, and you want to fine-tune a BERT-based classification model on a custom dataset.

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import torch
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from torch.utils.data import DataLoader
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from transformers import BertForSequenceClassification, BertTokenizer, AdamW
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tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
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model = BertForSequenceClassification.from_pretrained("bert-base-uncased").cuda()
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texts = ["I love this!", "This is terrible.", "..."]  # sample data
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labels = [1, 0, ...]  # 1=positive, 0=negative
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encoded = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
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dataset = torch.utils.data.TensorDataset(encoded["input_ids"], encoded["attention_mask"], torch.tensor(labels))
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dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
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optimizer = AdamW(model.parameters(), lr=5e-5)
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gradient_accumulation_steps = 4  # Adjust according to your needs
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for epoch in range(3):
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    for step, batch in enumerate(dataloader):
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        input_ids, attention_mask, label = [t.cuda() for t in batch]
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        outputs = model(input_ids, attention_mask=attention_mask, labels=label)
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        loss = outputs.loss
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        loss = loss / gradient_accumulation_steps
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        loss.backward()
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        if (step + 1) % gradient_accumulation_steps == 0:
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            optimizer.step()
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            optimizer.zero_grad()
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    print(f"Epoch {epoch+1} completed.")

By dividing the loss by 4 and accumulating gradients, we simulate a batch size that is 4 times larger than fits in memory at once.

11.1 Profiling and Fine-Tuning#

Use tools like PyTorch’s torch.profiler or NVIDIA Nsight to identify hotspots in your code. Optimize your dataloader (e.g., parallel workers, caching) and consider specialized kernels for matrix multiplications.

11.2 Specialized Hardware#

• NVIDIA Tensor Cores: Utilize FP16 or BF16 for faster matrix operations on supported GPUs.
• TPUs: Google’s Tensor Processing Units can be highly efficient for large-scale training, though typically used in cloud settings.
• FPGAs or Custom ASICs: An emerging area, but not typically for casual budgets.

11.3 Distributed Inference Services#

When you need to serve large volumes of inference requests, you can distribute the workload across multiple nodes. Tools like Ray Serve, Kubernetes, or custom microservices with gRPC can load-balance the requests, each node running a portion or a quantized version of the model.

11.4 Memory and Compute Trade-Offs#

• If your memory is highly constrained, you may need to accept longer latencies.
• If latency is paramount, you might keep the model in higher precision or replicate it across multiple GPUs to handle concurrent requests quickly.