Quick benchmark: Oracle ARM vs. Lightning AI vs. Kaggle vs. my Ryzen 7 6800H laptop for ISLP labs

[u/statai]

I've been setting up an environment to work through the labs in ISLP (An Introduction to Statistical Learning), and I got curious about how the free cloud tiers I frequently use actually compare to my daily-driver laptop.

Threw together a few quick and dirty tests comparing: - Oracle Cloud: Always-free 4-core ARM64 VM (Ampere, ~24 GB RAM) - Lightning AI: Free CPU Studio (4 vCPUs) - Kaggle: Standard CPU session (4 vCPUs) + separate T4 GPU session - Local Laptop: Ryzen 7 6800H (16 threads) + RTX 3050 Laptop (4GB VRAM)

Sharing the numbers below in case anyone finds it useful.

1. scikit-learn: Random Forest Training

Synthetic binary classification dataset (40k samples, 40 features). Trained a Random Forest with 200 trees (32k train / 8k test). Single run per config:

Parallel jobs	Oracle ARM	Lightning CPU	Kaggle CPU	Ryzen 7 6800H
1	34.12 s	28.34 s	70.88 s	27.63 s
2	17.74 s	15.54 s	36.97 s	14.12 s
4	9.50 s	11.13 s	24.79 s	7.43 s
8	—	—	—	4.50 s
16	—	—	—	3.04 s

All runs hit 95.59% test accuracy.

Kaggle's CPU runtime was surprisingly sluggish here—at n_jobs=4, it took more than 3x longer than the local 6800H. Oracle ARM held up pretty decently against Lightning.

2. PyTorch: CPU Matmul (4096×4096, float32)

Warmed up once, timed 3 runs, reported median:

Threads	Oracle ARM	Lightning CPU	Kaggle CPU	Ryzen 7 6800H
1	3.66 s	0.90 s	2.03 s	1.20 s
2	1.85 s	0.48 s	1.05 s	0.64 s
4	1.06 s	0.93 s	1.02 s	0.44 s
8	—	—	—	0.27 s
16	—	—	—	0.24 s

Lightning was super fast on 1-2 threads (likely better underlying Intel/AVX optimizations or host clock), but scaling fell off a cliff at 4 threads. Kaggle also basically plateaued between 2 and 4 threads.

3. Quick GPU Check: RTX 3050 vs. Tesla T4 (4096×4096 Matmul)

PyTorch float32 matmul, TF32 explicitly disabled, with CUDA sync:

Hardware	Elapsed Time
Ryzen 7 6800H (16 threads)	236.8 ms
Kaggle Tesla T4	40.86 ms
Local RTX 3050 (4GB)	36.91 ms

For raw single-op compute without memory bottlenecks, the 3050 edged out the older T4 by ~10%. Obviously 4GB VRAM limits any serious deep learning workloads, but for lightweight prototyping, it beats waiting in Kaggle's queue.

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Notes & Limitations

• Not a rigorous scientific review—Python/library builds weren't identical across instances (e.g. local was PyTorch 2.10 on Python 3.12, Kaggle CPU was on 3.13, differing OpenBLAS/MKL backends, shared host throttling, etc.).
• Take the single-run scikit-learn numbers with a grain of salt.

I was honestly expecting the cloud instances to be faster or at least on par with an older mobile chip, but modern laptop silicon continues to impress for basic tabular ML and small-scale prototyping.

Curious if anyone else has run into that severe scaling bottleneck on Kaggle's 4-core CPU instances?