 for example, suppressing the high-resource direction degrades Galician and Afrikaans translation, while the reverse intervention has almost no effect (swiss-ai/multilingual-geometry). This gives a mechanism behind the coverage picture of Section 5.4 and suggests that long-tail quality needs more than additional data.
Further platform studies cover error-predicting probes with calibrated inference-time steering (swiss-ai/apertus-probes) (Hedström et al., 2025), emotion persona representations (van der Ben et al., 2026), and bias auditing of latent generation paths (Pelossi et al., 2026).
5.10 Qualitative Spot-Testing
Hand testing during development repeatedly surfaces behavior that the benchmark suite misses, including degeneration on out-of-distribution prompts and weak multi-turn instruction following; this section documents that practice and its findings. User reports on Apertus 1.0 concentrated on a failure mode the benchmarks had not measured: degeneration in long conversations, particularly under language switching. Hand testing of Apertus 1.5 adds identity confusion, where the model misattributes itself to another provider in some languages (Section 7.2). The SFT mixture references the Apertus identity (Section 4.2) but contains no dedicated identity data, and occasional misattribution appears, plausibly from model-generated content in the web-scale pretraining data. The degeneration reports feed back into the evaluation suite as an automated degeneration metric, an n-gram repetition ratio computed on the generations of a small set of benchmarks of varying difficulty. Two further practices support it: every release candidate passes an automated compatibility suite of roughly 65 checks before distribution, covering prompt and begin-of-sequence invariants, degeneration probes, multimodal inputs, reasoning-channel separation, and the tool-calling and streaming paths; and we bisect behavioral regressions by serving every post-training stage side by side and comparing them on the same prompts.
6 Infrastructure, Scaling, and Efficiency
Whereas the Apertus 1.0 report documents the Alps Research Infrastructure in greater detail, this chapter focuses on what the 1.5 campaign adds. The introduction of new modalities and an expanded vocabulary brings additional infrastructure costs, while asynchronous reinforcement learning introduces a new class of workload. These runs share a production system whose scheduling environment, operating system, and storage infrastructure continue to evolve over the course of the project. We describe the platform and its operation (Section 6.1), the throughput and interruption records of the training runs (Section 6.2), their energy footprint (Section 6.3), and the engineering approaches used to address the challenges encountered (Section 6.4).
6.1 Infrastructure
Both models are trained on the Alps supercomputer at CSCS using a flexible reservation of GH200 nodes that grows from approximately 150 to 600 nodes as production ramps up. This allocation is shared across the Apertus 1.5 training campaign and related project workloads, including preparatory work for subsequent post-training stages. The production runs use 256 nodes for the 8B model and 512 nodes for the 70B model.
Alps, being a shared production system serving multiple users and projects, must also accommodate unrelated workloads alongside the Apertus campaign. For approximately two weeks in the middle of the project, one such full-scale workload requires the entire machine. During this period, training continues in the available night and weekend reservation windows, with schedules adjusted at short notice to match system availability.
All training jobs run in containers based on NGC images and integrated with the Alps software and networking environment through site-provided mechanisms. Library versions are pinned where required, and project-specific patches are applied on top of the validated software stack. During the project, the clust