Rapid Experimentation: 16–24x More Throughput Without Extra GPUs

Most LLM fine-tuning and post-training workflows still move like traffic on a one-lane road: you pick a configuration, train for a long stretch, check results, guess a tweak, and queue up the next run. By the time you’ve learned enough to be confident, you’ve spent most of your budget on the wrong roads. RapidFire AI changes the rules of the road. Instead of waiting hours to find out one answer, you get many answers at once, and you can use those early signals to steer your budget toward what matters.

TL;DR

RapidFire AI lets you compare multiple SFT configs concurrently—even on one GPU—so you get early, apples‑to‑apples curves and can control the runs dynamically in flight to stop underperformers and clone-modify high performers. Expect 16–20X faster time‑to‑signal and fewer GPU‑hours vs. sequential sweeps, all with your existing Hugging Face stack.

Quick background: Supervised Fine-Tuning (SFT)

SFT adapts a pretrained LLM to a target task using labeled input→output pairs (e.g., instruction → assistant response). In practice, teams commonly use Hugging Face Transformers/TRL with PEFT (LoRA/QLoRA) to fit larger models on limited GPUs. The typical steps are: format data with system/user/assistant roles, pick a base model, attach adapters, set trainer hyperparameters, and evaluate with deterministic decoding so curves are comparable. The aim is to align behavior, tone, and domain knowledge to your application’s metrics while controlling cost.

Figure 1. SFT in brief

Common problems with today’s SFT workflows

Sequential, one-run-at-a-time: You test a single configuration across all your GPUs, wait hours, then try another variation—the throughput of exploring ideas is low.

• Late and noisy signal: Useful feedback arrives late; differing batch sizes, lr schedulers, LoRA knobs, or decoding can muddy apples-to-apples comparisons.

• Compute/GPU memory pressure: Large models force tiny batches or heavy quantization on one GPU; scaling up adds cost and operational complexity.

• Orchestration & reproducibility friction: Checkpoint and config management for multiple trials is often manual, cumbersome, and brittle.

• Under-exploration of the search space: Slow iteration means too few config variations are explored and better options get missed, squandering your precious labeled data and lowering the impact on your use case.

Bottomline: Sequential SFT slows you down; testing one config at a time, waiting for hours, guessing what to try next, rinse and repeat. RapidFire AI collapses that process by letting you compare multiple configurations concurrently no matter how many GPUs you have, dynamically control runs in flight, and reuse progress—all on top of your existing PyTorch + Hugging Face (Transformers+PEFT+TRL) stack.

SFT with RapidFire AI

The following example shows how RapidFire AI improves SFT for a customer support Q&A chatbot by exploring many configs in parallel, pruning weak runs, and cloning and warm‑starting winners—delivering better scores in the same GPU time. A complete notebook for this example is available on GitHub.

We begin with the Bitext customer‑support dataset—a compact, well‑labeled English instruction→response corpus with intents/entities—because it’s realistic yet small enough for fast, apples‑to‑apples SFT comparisons. You can check out the dataset card on Hugging Face: bitext/Bitext‑customer‑support‑llm‑chatbot‑training‑dataset.

To ensure fair comparisons and quick feedback, we select a compact train/eval slice so each run sees the same data under the same conditions. Every example is formatted as a short chat with a system instruction, a user message, and the target assistant reply. This consistent conversational structure lets models focus on learning behavior and tone, and it keeps evaluation apples‑to‑apples across all candidates.

Next, we define two LoRA adapter capacities—one narrow (lower rank, fewer target modules) and one wider (higher rank, more target modules)—and pair them with two proven instruction‑tuned base models: Llama-3.1-8B-Instruct and Mistral-7B-Instruct-v0.3 (Table 1). 

Table 1. Two LoRA adapter capacities paired with two base models.

A common training recipe and deterministic evaluation keep the learning curves directly comparable across architectures and capacities, so differences you see reflect modeling choices, not setup drift.

Rather than launching one run after another, RapidFire treats the four combinations as a single grid and starts all of them immediately. This compresses time‑to‑first‑signal from hours to minutes on a single GPU and trims orchestration overhead. From the dashboard, you can stop obvious laggards after the first chunk, clone leaders to try wider targets or a different LR, and warm‑start those clones to preserve learning. In practice, pruning early saves roughly 50–75% of remaining epochs for weak configs and focuses compute where it matters.

Under the hood, RapidFire splits the training data into chunks and cycles the GPU across runs at chunk boundaries. This exposes early, per‑chunk signals for every run while keeping swap overhead low via cached models/adapters, so the GPU stays hot instead of idling (see Figure 2).

As training progresses, all metrics (loss and any text‑based evals) stream to the ML metrics dashboard, which is a fork of MLflow (see Figure 3). Overlaying curves makes apples‑to‑apples decisions straightforward; deterministic decoding ensures fairness across runs and chunks.

Figure 2. Overlay curves for loss across runs to compare fairly.

Finally, IC Ops let you dynamically control runs mid‑experiment, Stop laggards to free GPU time, Resume paused runs later if you’d like to revisit them, or Clone‑Modify a promising run—optionally warm‑starting it—to explore nearby ranks/targets/learning rates and reach lower loss in a fraction of time (see Figure 2, bottom).

With RapidFire AI, you can observe all four models’ performance and learning on the initial data chunks simultaneously. In our example, we started with 4 initial models. Then we cloned and modified the five promising models, stopped the laggards, and Mistral emerged as the top performer (Figure 2). This early signal allowed us to terminate underperforming runs and dynamically reallocate GPUs resources to explore other variations of the promising configs.

Figure 3. Stop laggards (in this case, M2) at the next chunk boundary to free GPU time.

Figure 4. Clone a promising run (M4 → M10), tweak knobs, and optionally warm‑start.

RapidFire AI Turns Sequential SFT into Concurrent Adaptive Exploration

See early SFT curves for every run with chunked concurrency:
RapidFire automatically splits your SFT training set into randomized, representative chunks and cycles candidate models/adapters so each sees instruction→response examples early—even on one GPU. Compare SFT loss and text‑based eval after the first chunk, instead of waiting for a single long run to finish.

• Act mid‑run safely for SFT with IC Ops at chunk boundaries:
  Stop underperforming SFT runs to free GPU time, Resume any later, or Clone‑Modify a promising SFT config (e.g., LoRA rank/targets, learning rate, prompt formatting). Warm‑start clones (if they have the same base model and adapter architecture) to retain learned SFT progress and reach better loss and eval metrics much sooner.
  
  
  

• Specify multiple SFT variations in one go with List/Range:
  Declare multiple SFT knob values—base model, LoRA rank/targets, learning rate, batch schedule, prompt formatting, etc.—using List or Range depending on whether the knob is categorical or numeric. Use grid search, random search, or AutoML heuristics to tell RapidFire how to expand them into separate SFT runs.It will evaluate them with deterministic decoding (fixed seeds and generation settings) so curves are directly comparable.
  
  
  

• Compare SFT runs fairly in your existing stack:
  Use your Transformers/PEFT/TRL SFT trainer codebase with an MLflow‑based metrics dashboard with integrated Interactive Control (IC Ops). Overlay, filter, and recolor loss and text metrics across SFT runs with consistent legends—no custom orchestration required.

Conclusion

RapidFire AI turns sequential SFT workflows into concurrent, adaptive exploration. With chunked execution, deterministic evaluation, and IC Ops (Stop, Resume, Clone‑Modify with warm starts), you can get earlier, clearer signals, waste fewer epochs on weak options, and drill down rapidly on winners. By cycling configs across data chunks, it surfaces early performance for every config; by using a small grid initially, you can better control how much to explore the space vs. drill down into promising regions; and by applying IC Ops, you can stop GPU waste early and accelerate winning paths with clone‑modify and warm‑starts.

Without RapidFire AI, you get signal more slowly, waste more epochs, and drill down slowly, often to the point of exhaustion and leaving a lot of accuracy on the table for your use case. With RapidFire AI, you  get better signals earlier, can act sooner, and focus your compute where it matters—on one or multiple GPUs. In practice, stopping three of four candidates after the first chunk can save ~75% of their remaining epochs; warm‑starting two clones of the leader avoids full re‑trains. Together, these mechanics can amplify your experimentation throughput by an order of magnitude,  yield 2–4× lowertime‑to‑useful‑model, and substantially fewer GPU‑hours versus sequential SFT.

Ready to try it? Explore the docs and QuickStart to set up your first RapidFire‑powered SFT experiment:

• RapidFire AI OSS documentation

• Quickstart Video