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Kolibri can run out of VRAM even though it activates only 3.46 billion parameters per token: its full model has 78 billion parameters, and Aleph Alpha estimates the FP8 weights alone occupy about 78 GB. Diagnose the point where startup fails before changing settings. A weight-loading failure is a capacity problem; a later KV-cache allocation failure may improve with a shorter context or less serving load.
Why Kolibri needs so much VRAM
Kolibri is a mixture-of-experts model. Its 3.46B active parameters per token describe how many parameters are active for a token, not how much of the model must be available to the serving system. Aleph Alpha lists 78B total parameters and estimates an FP8 weight footprint of approximately 78 GB. That is a model-card specification, not an independent benchmark or a complete estimate of runtime memory.
Inference also needs memory for the KV cache, activations, and runtime working buffers. The amount available to the process can be less than a GPU’s labeled capacity because other processes and runtime reservations consume memory too. Consequently, a system that appears to have enough memory for the weights may still fail during cache allocation or later startup work.
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Keep the complete startup log and identify whether the failure occurs while loading checkpoint weights, profiling memory or allocating the KV cache, or during graph capture and warm-up. Those stages point to different causes; a generic out-of-memory message is not enough to choose a reliable fix.
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- Weight loading: the available memory or supported device arrangement may not accommodate the model weights.
- KV-cache allocation: sequence length and serving load may be demanding more cache memory than is available.
- Graph capture or warm-up: startup may need additional memory for these operations.
NVIDIA’s troubleshooting guidance describes general NIM and vLLM memory issues, not a Kolibri-specific tested fix. Verify that any suggested setting is supported by the Kolibri plugin and runtime version you use. NVIDIA NIM troubleshooting guidance
If Kolibri fails while loading weights
Compare the free memory across the GPUs in your system with Kolibri’s published FP8 weight estimate and hardware examples. Aleph Alpha’s model card lists these minimum FP8 examples: 2× A100 80 GB, 2× H100 SXM5, 1× H200, 1× B200, or 1× B300. Its recommended configurations are 2× H100 SXM5, 2× H200, 1× B200, or 1× B300. These are provider-listed examples, not a guarantee that every runtime, configuration, or workload will fit.
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If the weights themselves cannot be loaded, reducing the context length will not solve the underlying capacity shortfall. Use a device configuration and model format documented as compatible with the provider’s runtime; do not assume an unofficial quantization or a consumer GPU will work unless that combination is documented. Aleph Alpha’s Kolibri-1 model card
If the failure occurs during KV-cache allocation
Check the configured maximum sequence length and the serving concurrency. Longer contexts and more simultaneous requests can increase cache pressure. Aleph Alpha recommends serving Kolibri at no more than 262,144 tokens for efficiency and complex tasks. A shorter maximum context may help with a KV-cache capacity failure, but it does not reduce the model’s FP8 weight files.
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Avoid lowering --gpu-memory-utilization blindly: NVIDIA notes that doing so can reduce the memory budget available for the KV cache and worsen a cache-capacity problem. Treat this as general vLLM/NIM guidance, and confirm the flag’s behavior in the Kolibri package version you are running. NVIDIA NIM troubleshooting guidance
If startup fails during graph capture or warm-up
Graph capture and warm-up can require additional memory beyond loading weights and allocating the cache. NVIDIA’s general guidance discusses reducing the memory budget or disabling CUDA graphs as diagnostic options for some failures, but those controls are backend-specific and are not established as guaranteed Kolibri fixes. Use the logs to identify the failing operation and prefer configuration documented for the Aleph Alpha runtime.
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Use Kolibri’s documented serving stack
Aleph Alpha says Kolibri requires the aleph-alpha-inference package, which supplies the Kolibri vLLM plugin. Its model card documents this installation command:
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorspip install 'aleph-alpha-inference>=1'The model card also documents a vLLM launch using Kolibri’s reasoning and tool-call parsers. Use the current launch instructions and compatibility notes there rather than copying a generic vLLM command: the supported options can depend on package and runtime versions.
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- System Compatibility Note: 2-slot card, 271x112x39mm, single 8-pin power, 200W TDP. Verify chassis clearance and PSU capacity before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
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Configure context lengths above 262,144 tokens
The model card lists a maximum context of 1,048,576 tokens and documents extra configuration for serving beyond 262,144 tokens. For that longer-context setup, it specifies --max-model-len 1048576 and this Hugging Face override:
--hf-overrides '{"max_position_embeddings": 1048576}'A larger context is not automatically a better setting: use it only when the task requires it and the available memory can support the resulting cache demand. Aleph Alpha recommends no more than 262,144 tokens for serving efficiency and complex tasks. Aleph Alpha’s Kolibri-1 model card
What to compare before trying again
- Usable accelerator memory: check free memory after other processes and runtime reservations, not only the GPU’s advertised capacity.
- Device count and arrangement: compare the system with the provider’s listed configurations and ensure the runtime supports that arrangement.
- Failure stage: distinguish weight loading from KV-cache allocation and graph or warm-up errors.
- Context and serving load: set sequence length and concurrency to match the workload and available memory.
- Runtime compatibility: install the required Aleph Alpha plugin and verify generic vLLM settings against its version-specific documentation.
The published specifications describe the model and its documented serving setup; they do not establish successful operation on a particular consumer GPU, Mac, third-party runtime, or unofficially quantized build.
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