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Kolibri has about 78.1 billion parameters in total, even though Aleph Alpha lists about 3.46 billion active parameters per token. “Active” describes the weights participating in a token’s computation—not the complete model that must be available to run it. Aleph Alpha’s model card lists roughly 156 GB of BF16 weight memory, so Kolibri does not fit into memory sized for a 3.46-billion-parameter model.
What does Mixture-of-Experts mean?
A Mixture-of-Experts (MoE) model contains multiple expert components and routes each token to a selected subset of them. Instead of using every expert for every token, the model activates only some experts during that token’s computation. That is why an MoE model can have a large total parameter count and a much smaller active-parameter count per token.
Aleph Alpha describes Kolibri as a 50-layer MoE transformer. Its model card lists 384 experts per layer, including one shared expert and six routed experts. The counts describe the model’s architecture; they do not mean that all 384 experts are selected for every token. The card does not establish a precise accounting breakdown of how every component contributes to the active-parameter total, so that figure should not be reverse-engineered from the expert counts.
In plain terms, total parameters are the model’s full learned-weight inventory. Active parameters per token are the portion used for an individual token’s computation. Routing changes which experts participate; it does not make the other weights disappear from the model.
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Why does 3.46B active parameters not mean 3.46B-sized memory?
Because the active count is per token, while memory must hold the model’s weights—including experts that may be selected for other tokens. Aleph Alpha’s 2026 model card lists 78,103,074,560 total parameters and approximately 156 GB of BF16 weight memory. The 3,457,573,120 active parameters per token describe computation, not the full amount of model data that must be made available for inference.
The practical consequence is important: do not estimate Kolibri’s hardware needs as if it were an ordinary 3.46B-parameter model. The model card’s published BF16 requirements include 4× A100 80 GB, 4× H100 SXM5, 2× H200, 1× B200, or 1× B300 as minimum options. Its recommended configurations include 4× H100 SXM5, 2× H200, 2× B200, or 1× B300. These are Aleph Alpha’s configuration guidance, not independent compatibility testing; actual deployment also depends on software and serving setup.
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What the parameter figures do—and do not—tell you
| Figure | What it means | What not to infer |
|---|---|---|
| 78,103,074,560 total parameters | The full parameter inventory listed in Aleph Alpha’s model card (2026). | It is not the number of parameters used for every token. |
| 3,457,573,120 active parameters per token | The active-parameter count Aleph Alpha lists for an individual token’s computation (2026). | It is not the full model size in memory, nor does it establish a guaranteed speedup, price reduction, or quality level. |
| About 156 GB BF16 weight memory | The model card’s approximate BF16 weight-memory figure (2026). | It is not a complete estimate of every deployment’s total memory needs. |
MoE routing can reduce how much of the model participates in a token’s computation compared with activating every expert, but the active-parameter ratio alone does not predict end-to-end throughput, cost, or answer quality. Those depend on the serving system and evaluation conditions, among other factors.
How to read Kolibri’s context-length figures
Aleph Alpha’s model card lists a maximum context length of 1,048,576 tokens. It also recommends serving contexts of no more than 262,144 tokens for efficiency and complex tasks. The maximum is therefore not the same as the provider’s recommended operating point. Long contexts can also add deployment demands beyond the weight memory figure, so 156 GB should not be treated as a complete system-memory budget.
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What Kolibri is intended to do
The model card describes Kolibri as focused on English and German, with explicit reasoning mode and tool calling. Aleph Alpha lists intended uses including multi-step reasoning, retrieval-augmented generation, coding, long-document processing, and agentic tool calling. These are provider-described capabilities and use cases, not a guarantee of results on a particular task or deployment.
The card also describes a training sequence of 20 trillion pre-training tokens, followed by 3.44 trillion mid-training tokens and 201 billion tokens for long-context extension. Those figures provide training context; they do not by themselves establish performance for a given workload.
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How to interpret Kolibri’s published benchmarks
In its launch comparison, Aleph Alpha reports an English AIME 2025 score of 96.9 and a GPQA Diamond score of 84.3. These are Aleph Alpha’s reported benchmark results, not independent measurements. They should be compared with other results only when the benchmark version, language, setup, and scoring conditions match; the numbers alone do not demonstrate how Kolibri will perform on a reader’s own tasks.
Aleph Alpha’s launch article, “Kolibri Has Landed: A Sovereign Open-Weight Model,” published 3 October 2026, summarizes the model as an “English-German Mixture-of-Experts Transformer with 78B total parameters, 3B active” and says it supports context lengths of up to 1M tokens. The model card gives more precise parameter counts and distinguishes the maximum context from its shorter serving recommendation.
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What to compare when evaluating an MoE model
Active parameters are useful, but they are only one part of a practical comparison. For Kolibri or another MoE model, check:
- Total parameters and active parameters per token: distinguish the full weight inventory from the subset used in token computation.
- Weight memory at the intended precision: BF16 and other formats can have different memory requirements.
- Context limits: separate a stated maximum from a recommended serving context.
- Serving throughput and cost: compare under similar hardware, software, workload, and context conditions rather than inferring them from parameter counts.
- Languages, tasks, and benchmarks: match the language, benchmark version, and evaluation setup, and note whether results are vendor-reported or independently measured.
Aleph Alpha lists Kolibri under the Apache 2.0 license and gives 3 October 2026 as its release date. Availability, hardware guidance, and software compatibility can change; consult the Kolibri model card for the provider’s current details and the Aleph Alpha launch article for its published benchmark comparisons.
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