: an outreach and community function connects external contributors and deployment engineers with the development process and collects their findings to improve model compatibility. Smith et al. (2026) argue that inclusive design and capacity building are conditions for responsible openness. The public website1313
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https://apertus-ai.org includes the charter, usage guides, benchmark resources, model documentation, and links to downstream projects; newsletters, social media, and multilingual media monitoring complement it. Direct involvement of the engineering team has included presentations to science and industry, round-tables with companies, support of hackathons, and hands-on workshops.
One month before the Apertus 1.5 release, in addition to the third-party safety evaluation of Section 5.9, 15 organizations, represented by roughly 50 engineers and experts, evaluated the 8B and 70B weights for deployment readiness: quantization, alignment, multimodal input, tokenizer distribution, and general compatibility. Their feedback led to quantizations and community builds for several inference frameworks.
The pre-release surfaced problems that internal testing had missed, and we thank Infomaniak and OnPrem.ai in particular. Infomaniak contributed a vLLM integration that runs the image and audio tokenizers on the GPU. Our review of it found that encoding several media items as one zero-padded batch changes their codes, because neither tokenizer satisfies crop⁡(T⁡(pad⁡(x)))=T⁡(x)\operatorname{crop}(T(\operatorname{pad}(x)))=T(x), so the integration now encodes each item separately. OnPrem.ai traced a tool-calling failure in the 70B pre-release checkpoint, where a faulty generation configuration let the model continue past a complete tool call and fabricate the tool’s result, and confirmed the corrected configuration in the release candidate. Their quantization studies showed that 8-bit (FP8) weights and activations are near-lossless. 4-bit NVFP4 in the attention layers of the 8B model produces NaN logits, which they attribute to the quadratic term of xIELU amplifying quantization noise. Keeping attention at FP8 and quantizing only the MLP to NVFP4 fits the 70B model into 49 GiB, so it runs on a single 96 GB GPU with more than 200k tokens of context.
We also measured the serving performance of the 70B model at five inference providers and on our own deployment with a single run of the open-source tool GuideLLM from CSCS on 16 September 2026. Table 41 lists latency, throughput, and time to first token, with providers anonymized; requests originate at CSCS, which gives our own deployment a network advantage.
Table 41: Serving performance of Apertus 1.5 70B on a simulated workload, by anonymized inference provider. Latency in seconds and TTFT (time to first token) in milliseconds, lower is better; throughput in tokens per second, higher is better.
Throughput (tokens/s)
Provider
Latency (s)
TTFT (ms)
Input
Output
P0 (ours)
1.90
103.6
179.7
67.9
P3
2.09
87.9
157.6
61.5
P5
2.18
171.2
150.9
58.9
P1
2.31
469.8
143.6
56.4
P4
3.63
81.6
92.8
35.3
P2
4.30
223.6
79.5
29.9
For replies of the same length, latency varies by a factor of 2.3 between deployments (1.9 to 4.3 s) and time to first token by a factor of almost six (82 to 470 ms). Comparing answer quality across providers proved harder than measuring speed: most of our benchmarks score answers by log-likelihood, which requires a completions endpoint that several providers and our own gateway do not expose, and rate limits slow the remaining generation-based tests. Therefore, a quality comparison between providers remains open.
8 Conclusion
Apertus 1.5 extends the Apertus 1.0 models from text to image and audio understanding through continued pretraining on 4T and 2T additional tokens, extends the context window fourfold to 262,144 tokens, and introduces the second generation of our post-training pipeline, which adds a thinking mode, more reliable instruction following, and more effective too