Beirit Harvey

This blog breaks down what training and inference actually are, how they differ, and why it matters when you’re planning GPU capacity, SLAs, and budgets.

Inference vs. Training: What’s the Difference?

When people talk about “AI workloads,” they usually mean one of two things: training or inference.

They usually use related models and often the same hardware family, but they behave very differently in terms of cost, performance requirements, and how you design your infrastructure.

This blog breaks down what training and inference actually are, how they differ, and why it matters when you’re planning GPU capacity, SLAs, and budgets.


What Is Training?

KJ Jacoby notes, Training is the proces

When people talk about “AI workloads,” they usually mean one of two things: training or inference.

They usually use related models and often the same hardware family, but they behave very differently in terms of cost, performance requirements, and how you design your infrastructure.

This blog breaks down what training and inference actually are, how they differ, and why it matters when you’re planning GPU capacity, SLAs, and budgets.


What Is Training?

KJ Jacoby notes, Training is the process of “teaching an AI system to perceive, interpret and learn from data. That way the AI will later be capable of inferencing – making decisions based on information provided.”

You start with:

 * A model architecture (e.g., a transformer)
 * A large labeled or partially labeled dataset
 * An objective (e.g., minimize prediction error)

During training, the model:

 1. Sees input data (like text, images, audio).
 2. Makes a prediction (forward pass). The forward pass is the step where input data flows through all the layers of a neural network to produce an output. It’s often referred to as forward propagation. During training, it produces the model’s predictions, which are then compared to the ground-truth labels to calculate the error (loss).
 3. Compares that prediction to the correct answer (loss calculation).
 4. Adjusts its internal weights to do better next time (backward pass + gradient updates).

This loop runs over and over, often across:

 * Billions or trillions of tokens
 * Millions of images or samples
 * Hours, days, or weeks of GPU time for large-scale models


Characteristics of Training Workloads

Training jobs are typically:

 * Compute-intensive
   * Both forward and backward passes run on every batch.
   * You often use mixed precision (FP16, BF16, FP8) for speed and efficiency.
 * Throughput-oriented
   * You care about how fast you can process massive amounts of data.
   * Latency per individual request matters less than overall time-to-train.
 * Batch-friendly
   * Larger batch sizes help keep GPUs fully utilized.
   * Very big models may require model parallelism and pipeline parallelism. Hugging Face documentation explains, “model parallelism distributes a model across multiple GPUs” and “...is a useful strategy for training models that are too large to fit into the memory of a single GPU.” Pipeline parallelism is “similar to model parallelism, but it’s more efficient because it reduces the amount of idle GPU time.”
 * Flexible in timing, but long-running
   * A single training run can last hours to weeks.
   * You may run multiple experiments, hyperparameter sweeps, and fine-tunes.
 * Sensitive to hardware and networking
   * High-bandwidth, low-latency interconnects matter for multi-node training.
   * High-memory GPUs let you train larger models or use longer sequence lengths.


What Is Inference?

Inference is what happens after a model is trained and deployed. When you run inference, the model takes fresh input and runs it through the network, using patterns it encoded during training in its internal parameters (weights). The exact output depends on the application: it might classify an email as spam, transcribe audio into written text, or turn a long article into a short summary. In all cases, inference is about computing a result that can immediately drive a decision or action.

You send the model:

 * A prompt: “Write an intro email to a new customer.”
 * An image: “Is there a cat in this photo?”
 * A user event: “What product should we recommend next?”

The model:

 1. Takes in the request.
 2. Runs a forward pass to compute an output.
 3. Returns a prediction, completion, classification, or recommendation.

No gradients, no weight updates, just using what it has already learned.


Characteristics of Inference Workloads

Inference jobs are typically:

 * Latency-sensitive
   * Users expect a response in milliseconds to seconds, not minutes.
   * Lower latency → better UX and higher conversion.
 * Often spiky or unpredictable
   * Traffic follows user behavior: time of day, product launches, seasonality.
   * You need to handle peaks without overpaying during quiet periods.
 * Cost-sensitive at scale
   * A single model might serve millions of requests per day.
   * Small inefficiencies in per-request cost can add up quickly.
 * Optimization-heavy
   Common techniques:
   * Quantization (e.g., INT8, FP8) to reduce memory and improve throughput.
   * Model distillation to create smaller versions of large models.
   * Caching, batching, and dynamic routing to utilize GPUs efficiently.
 * Diverse in patterns
   * Some inference is real-time (chatbots, fraud detection).
   * Some is offline or batch (daily recommendations, report generation).
   * Some is multi-node (very long contexts or large, sharded models).


Key Differences: Training vs. Inference

Here’s a simple way to think about it:

 * Goal
   * Training: Teach the model.
   * Inference: Use the model.
 * Compute Pattern
   * Training: Forward + backward passes, frequent updates, many epochs.
   * Inference: Forward-only, no weight updates.
 * Resource Needs
   * Training:
     * High GPU memory for large models and long sequences
     * Fast storage for huge datasets and checkpoints
     * High-speed networking for multi-node jobs
   * Inference:
     * Sufficient GPU memory for the served model(s)
     * Strong focus on latency, concurrency, and throughput
     * Efficient autoscaling or scheduling for spiky traffic
 * Time Profile
   * Training: Long-running, predictable jobs.
   * Inference: Short-lived, request-driven workloads.
 * Optimization Focus
   * Training: Reduce time-to-train, improve model quality.
   * Inference: Reduce cost-per-request, improve latency & reliability.


How Infrastructure Requirements Differ

Because training and inference behave so differently, they often benefit from distinct infrastructure strategies.


For Training

You typically want:

 * High-memory GPUs
   To fit:
   * Larger models per GPU
   * Longer sequence lengths
   * Bigger batch sizes for better GPU utilization
 * Fast, scalable storage
   * To stream large datasets
   * To write frequent checkpoints without stalling training
 * Fast networking
   * RDMA-capable or Ethernet optimized for AI-scale clusters
   * Low latency and high bandwidth to keep GPUs in sync
 * Robust job scheduling
   * Systems like Slurm or Kubernetes for:
     * Queuing long-running jobs
     * Fair sharing across teams
     * Checkpointing and resuming big experiments


For Inference

You design differently:

 * Right-sized models per GPU
   * Possibly smaller distilled or quantized versions of your main model.
   * Multiple models or tenants sharing a single GPU.
 * Latency-aware routing and batching
   * Smart batching to increase throughput without wrecking response times.
   * Request routing based on model type, priority, or customer tier.
 * Autoscaling and elasticity
   * Scale up GPU capacity when traffic spikes.
   * Scale down when demand is low to control costs.
 * Monitoring focused on UX and cost
   * Metrics like p50/p95 latency, error rates, GPU utilization, and cost per 1K requests.


Where Training and Inference Overlap

Despite their differences, training and inference aren’t totally separate worlds:

 * Fine-tuning blurs the line
   * You take a base model and run smaller training loops on your own data.
   * This combines elements of both: heavy training logic, but often closer to production constraints.
 * Online learning or reinforcement learning
   * Some systems keep updating models based on live data or feedback.
   * That means training and inference might run side by side.
 * Shared observability and MLOps
   * Pipelines for logging, versioning, monitoring, and rollback are needed for both.
   * You still need to track which model version is where, and how it’s performing.


How TensorWave Helps With Both Training and Inference

Understanding the difference between training and inference is one thing. Running both reliably at scale, on the same cloud, is another.

That’s where TensorWave comes in.

TensorWave is an AMD-only GPU cloud designed specifically for modern AI workloads, from long-running training jobs to latency-sensitive inference.

On the training side, TensorWave focuses on:

 * High-memory AMD Instinct™ GPUs so you can fit larger models and longer contexts per node, instead of stitching everything across a sprawling cluster.
 * High-throughput, shared storage tuned for checkpoints, sharded datasets, and frequent reads/writes during large training runs.
 * Cluster-level scheduling that lets you run multi-node training jobs without constantly worrying about fragmentation or noisy neighbors.

On the inference side, the same platform is optimized to:

 * Serve production traffic with low latency, whether you’re running single-tenant deployments or shared, multi-tenant clusters.
 * Right-size capacity so you can place multiple models on the same GPU, mix real-time and batch inference, and avoid paying for idle capacity.
 * Reuse the same infrastructure across training, fine-tuning, and inference, so you’re not maintaining separate stacks for each phase of the model lifecycle.

Because training and inference share the same underlying GPU infrastructure on TensorWave, you can:

 * Train and fine-tune large models on high-memory AMD Instinct GPUs
 * Promote them into production on the same cloud
 * Run inference with the flexibility to adjust capacity as your traffic grows

Instead of treating training and inference as two separate worlds, TensorWave gives you a single, GPU-optimized platform that supports both; from your very first experiment to serving production workloads at scale.


About TensorWave

TensorWave is the AI neocloud purpose-built for performance. Powered exclusively by AMD Instinct™ Series GPUs, we deliver high-bandwidth, memory optimized infrastructure that scales with your most demanding models–training or inference. 

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Inference vs. Training: What’s the Difference?

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