If hitting GPU out-of-memory during model training feels like a tax on your ambitions, a new open-source optimizer experiment for PyTorch might be worth a look. At AI Tech Inspire, we spotted an optimizer dubbed ApexW that explores a simple proposition with real-world bite: shrink optimizer-state memory and, in the right conditions, step faster than PyTorch’s fused AdamW. The catch? It’s an engineering trade-off with clear sweet spots and equally clear pitfalls.
Quick facts from the announcement
- ApexW targets reduced optimizer-state memory and faster optimizer steps for large GPU workloads.
- It experiments with compact optimizer states and a Triton-based execution path.
- In tests with a few large tensors, the Triton + compact-state path was about 1.5x faster than PyTorch’s fused AdamW; optimizer-state memory was roughly cut in half.
- Benchmarks cited are optimizer-only, not full end-to-end training runs.
- The automatic execution mode doesn’t consistently reproduce the speedups.
- On workloads with many small tensors, ApexW was significantly slower than PyTorch’s fused optimizer. Example: 128M parameters across many small tensors saw PyTorch at 16.1 ms/step vs. ApexW at 76.0 ms/step (~0.21x speedup).
- In an end-to-end MLP test, PyTorch still ran slightly faster overall, credited to deeper C++ integration.
- The primary value proposition: halving optimizer-state memory may help avoid OOM and increase batch size or model size without new hardware—especially for Transformer/LLM fine-tuning.
- The author is seeking feedback, broader testing on realistic models and GPUs, and reviews of the implementation and benchmarks.
- GitHub: github.com/omen42515-prog/apexw
Why this matters: Optimizer state is a quiet memory hog
Most practitioners feel memory pressure from activations and parameters; the optimizer state is the quiet third wheel. AdamW maintains per-parameter moments (often two tensors, m and v), which can add roughly 2x parameter memory in the optimizer state alone. Even when you train in 16-bit, many setups keep optimizer states in 32-bit, so the footprint is substantial. On a tight 24–48 GB GPU, that overhead can be the difference between feasible and impossible.
If a compact state format can reliably cut that in half, it’s not a small win. That reclaimed memory can convert directly into higher batch sizes, larger sequence lengths, or even a bigger model—often the knobs that deliver the most meaningful throughput or quality improvements.
What ApexW tries: compact states + Triton kernels
ApexW explores two core ideas:
- Compact optimizer state: storing and updating the state in a space-efficient representation to reduce peak memory usage.
- Triton-based execution path: hand-rolled kernels via Triton to accelerate the optimizer step for large, contiguous chunks of memory.
Against the baseline of PyTorch’s fused AdamW (which is already fast and benefits from C++/CUDA-level fusion), the reported microbenchmarks show a roughly 1.5x speedup when the workload consists of a few large tensors. That is exactly the kind of shape you see in many Transformer-style models where parameters are dominated by big weight matrices rather than thousands of tiny tensors.
However, the Triton path appears sensitive to Python overhead and kernel launch patterns. On models with many small tensors—a common profile in certain MLPs or finely modular architectures—ApexW reportedly loses badly to PyTorch’s fused optimizer. That aligns with what many have seen when moving work from fused C++ kernels to Python-orchestrated custom kernels: launch latency becomes the ceiling.
“At 128M params across many small tensors, PyTorch native: 16.1 ms/step; ApexW compact: 76.0 ms/step (~0.21x).”
Speedups vs. reality: optimizer-only isn’t the whole story
Another important caveat: the best numbers are from optimizer-only benchmarks. In full training, the optimizer step is just a slice of the pie; forward and backward passes dominate. In an end-to-end MLP test, PyTorch reportedly remains slightly faster overall, thanks to its deeper C++ integration and mature fusion strategies.
That doesn’t negate the value—especially the memory savings—but it reframes it. If you’re compute-bound during backprop, shaving milliseconds off the optimizer might not move your wall-clock much. If you’re memory-bound, though, halving the optimizer state can be the unlock that lets you dial up batch size or context length and get more work done per step.
Who should try it first?
Two profiles stand out:
- LLM fine-tuning and Transformers: These often feature relatively few, large parameter tensors (big projection and attention matrices). If that matches your model’s shape, ApexW’s Triton path might land in its comfort zone. Think practical stacks built with Hugging Face or custom Transformer code in PyTorch.
- Teams living on the OOM edge: If your training runs frequently die in CUDA OOM, cutting optimizer memory may be a quick lever to unlock larger batches without downgrading precision or offloading to CPU/NVMe.
For perspective, consider a rough mental model: with 1B parameters, 16-bit weights consume about 2 GB; AdamW’s two 32-bit moment tensors add around 8 GB. If a compact-state approach saves half of that moment memory, you could claw back on the order of ~4 GB—enough to materially alter batch size or sequence length choices.
Where it struggles (for now)
- Many small tensors: Python loop and kernel launch overhead dominate; PyTorch’s fused AdamW remains the right call.
- Automatic mode inconsistency: The experiment’s auto path doesn’t consistently reproduce the large-tensor speedups, hinting that better heuristics or bucketing strategies are needed.
- End-to-end wins not guaranteed: On real models, the net outcome depends on how much the optimizer step contributes to total time vs. forward/backward.
These are solvable-in-principle issues. Techniques like parameter bucketing, fusing tiny tensors into contiguous buffers, and even using CUDA Graphs to reduce launch overhead are all candidates worth exploring.
How it compares to other levers
ApexW’s pitch is orthogonal to many popular tactics:
- 8-bit optimizers: Tools that quantize optimizer states can also reclaim memory, sometimes with trade-offs in stability or convergence. ApexW instead focuses on a compact representation plus custom kernel paths.
- ZeRO-style sharding and offload: Sharding or offloading optimizer state relieves memory but increases complexity and, at times, communication cost. ApexW keeps the action on-device.
- Mixed precision and compiler optimizations: These primarily speed forward/backward or activation memory. ApexW targets the optimizer step and its state specifically.
For many teams, a layered approach—e.g., standard mixed precision, compiler-level fusion, plus a memory-lean optimizer—can stack benefits.
Benchmarks we’d love to see next
To help the community assess where ApexW fits, here are pragmatic tests that would paint a clearer picture:
- End-to-end training on a mid-size Transformer (encoder-decoder and decoder-only), reporting tokens/sec and memory headroom.
- Parameter-shape sweeps: controlled mixes of few-large vs. many-small tensors to map the performance boundary.
- GPU coverage: A100/H100 vs. consumer GPUs (e.g., 3090/4090) to see how kernel launch characteristics vary across architectures.
- Profiler traces:
torch.profilerornsystraces highlighting launch overhead, memory bandwidth, and kernel occupancy. - Convergence checks: Compare loss curves vs. fused AdamW on standard tasks to ensure the compact-state math behaves as expected.
- DDP/ZeRO compatibility: Interactions with sharding, gradient accumulation, and common distributed patterns.
Bottom line from AI Tech Inspire
ApexW is an intriguing engineering swing at a real bottleneck. The microbenchmarks suggest that, when your model parameters live in a handful of big matrices, the 1.5x optimizer-step speedup and ~2x reduction in optimizer-state memory are attainable. Conversely, if your model scatters parameters across many tiny tensors, PyTorch’s fused AdamW remains the safer, faster choice.
In other words, know your parameter shape profile before you switch. If you’re training Transformers, large language models, or diffusion backbones akin to Stable Diffusion and architectures with big contiguous weights, ApexW could be the memory lever that lets you push batch size or sequence length higher without buying new hardware. If your graph is highly modular with lots of small parameters, consider this an R&D branch—not a drop-in speed win.
Curious? The code and ongoing discussion live here: ApexW on GitHub. If you’ve tuned fused optimizers in PyTorch, written Triton kernels, or profiled training at scale (GPT-style workloads included—see GPT-4 for context), your feedback could help validate whether this path offers a new wedge in the memory/speed trade space.
Key takeaway:
“If optimizer memory is your bottleneck and your parameters are big and few, ApexW is worth a trial run. If you’re parameter-fragmented, stick with PyTorch’s fused path for now.”
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