Model reference · open weights

MiniCPM5-DSpark

LLMs openbmb Text gen 2 builds Open weights 430 dl/mo

MiniCPM5-DSpark is an open-weight language model from openbmb. MiniCPM5-2B-DSpark (BF16) weighs 648 MB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • MiniCPM5-DSpark is a 2.6B parameter text-generation model developed by openbmb, designed to function as a draft model for speculative decoding with MiniCPM5-2B in llama.cpp.
  • The model supports English and Chinese and is distributed as a 653 MB BF16 GGUF file under the Apache-2.0 license.

Summary of the openbmb/MiniCPM5-2B-DSpark-GGUF model card, 2026-10-01. The estimate below is for another build of the family.

What it is

Released byopenbmb
Released2026-09-09
VRAM648 MB for the weights

What it runs on

Memory and cards for MiniCPM5-2B-DSpark (BF16)

648 MBweights, file size
5 MBcache per 1K tokens
647 MBruntime overhead, at least
131,072 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB24661all 128K11.6 GB
RTX 4060 Ti 16 GB33684all 128K15.4 GB
RTX 3090 24 GB527131all 128K23.4 GB
RTX 4090 24 GB525131all 128K23.4 GB
RTX 5090 32 GB708177all 128K31.0 GB
L40S 48 GB1000+254all 128K44.0 GB
A100 80 GB1000+458all 128K78.2 GB
H100 80 GB1000+434all 128K78.1 GB
RTX PRO 6000 Blackwell 96 GB1000+528all 128K93.8 GB
DGX Spark (GB10) 128 GB unified1000+609all 128K107 GB
H200 141 GB1000+791all 128K138 GB
B200 180 GB1000+1000+all 128K176 GB
Memory needed at each load
Requests at once8K tokens each32K tokens each
11.3 GB1.5 GB
51.5 GB2.1 GB
81.6 GB2.6 GB
162.0 GB4.0 GB
322.6 GB6.7 GB
644.0 GB12.0 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
MiniCPM5-2B-DSpark-GGUF ↗ GGUF 653 MBRTX 3060 12 GB
MiniCPM5-2B-DSpark (above) ↗ 324MBF16 648 MBRTX 3060 12 GB

From the model card

What openbmb says about MiniCPM5-DSpark

Read the model card

GGUF version of MiniCPM5-2B-DSpark for speculative decoding with MiniCPM5-2B in llama.cpp.

FilePrecisionSize
MiniCPM5-2.6B-DSpark.ggufBF16653 MB

Usage with llama.cpp

llama-server \
  -m MiniCPM5-2B-Q4_K_M.gguf \
  -md MiniCPM5-2.6B-DSpark.gguf \
  --spec-type draft-dspark --spec-draft-n-max 7 \
  -ngl 99 -ngld 99 -fa on \
  -c 8192 --jinja --port 8080

Limitations and Disclaimer

This model has no autonomous intent or legal personhood; its outputs are text generated from statistical patterns and may be inaccurate, biased, or offensive, and may be manipulated by carefully crafted prompts ("jailbreaks") into producing unintended content. Its responses on sensitive topics such as politics, health, finance, and law are not reviewed by experts and should not be treated as professional advice.

This model is provided "AS IS", without warranty of any kind, express or implied, and the developers are not liable for any damages arising from its use. Users must employ the model only for lawful, compliant, and ethical purposes, configure their own safeguards, and label AI-generated content where required; deliberate jailbreaking, injection attacks, or inducing harmful output is prohibited, and any such testing is at the user's own risk.

License

This repository and MiniCPM model weights are released under the Apache-2.0 License.

Citation

Please cite our paper if you find our work valuable:

@article{minicpm4,
  title={Minicpm4: Ultra-efficient llms on end devices},
  author={MiniCPM, Team},
  journal={arXiv preprint arXiv:2506.07900},
  year={2025}
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
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