Model reference · open weights

MiniCPM-V-4.7-DSpark

LLMs openbmb Text gen · MoE 3 builds Open weights 1 dl/mo

MiniCPM-V-4.7-DSpark is an open-weight language model from openbmb. MiniCPM-V-4.7-35B-A3B-DSpark-gguf (GGUF) weighs 237 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.

  • MiniCPM-V-4.7-DSpark is a draft checkpoint created by openbmb for speculative decoding with the MiniCPM-V-4.7-35B-A3B model.
  • It features 389M parameters and supports a context length of 262144 tokens.
  • The model is licensed under apache-2.0 and supports English and Chinese.

Summary of the openbmb/MiniCPM-V-4.7-35B-A3B-DSpark model card, 2026-10-13. The estimate below is for another build of the family.

What it is

Released byopenbmb
Released2026-10-08
Parameters389M
VRAM237 MB for the weights

What it runs on

Memory and cards for MiniCPM-V-4.7-35B-A3B-DSpark-gguf (GGUF)

237 MBweights, file size
651 MBruntime overhead, at least
262,144 tokenscontext max

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GBfits
RTX 4060 Ti 16 GBfits
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
MiniCPM-V-4.7-35B-A3B-DSpark ↗ 389MBF16 778 MBRTX 3060 12 GB
MiniCPM-V-4.7-1B-DSpark ↗ 214MBF16 427 MBRTX 3060 12 GB
MiniCPM-V-4.7-35B-A3B-DSpark-gguf (above) ↗
11 builds: Q4_0 230 MB … F16 789 MB
GGUF 237 MBRTX 3060 12 GB

From the model card

What openbmb says about MiniCPM-V-4.7-DSpark

Read the model card

MiniCPM-V-4.7-35B-A3B-DSpark is a DSpark draft checkpoint trained for exact pairing with MiniCPM-V-4.7-35B-A3B and its tokenizer.

Model Specification

PropertyValue
Target modelMiniCPM-V-4.7-35B-A3B
Draft layers5
Draft parameters389,223,169
Draft tokens per forward pass15
Target hidden-state layers[3, 11, 19, 27, 35]
Checkpoint precisionBF16

Training Recipe

Training responses were generated by MiniCPM-V-4.7-35B-A3B from a mixture of Vision & Text prompts.

SettingValue
Training sequences2M
Epochs6
OptimizerAdamW
ObjectiveCE + L1 + confidence loss

Evaluation

Acceptance length is defined as the total number of completion tokens divided by the total number of speculative verification steps. Evaluation uses natural EOS termination and max_new_tokens=8192.

DomainAccept Length
Vision
MMMU5.098
MathVista6.126
CharXiv_reasoning_val5.479
OCRBench5.407
Text
Math6.687
Code7.387
General3.501

Usage with vLLM

vllm serve \
  --model openbmb/MiniCPM-V-4.7-35B-A3B \
  --served-model-name MiniCPM-V-4.7-35B-A3B \
  --tensor-parallel-size 2 \
  --max-model-len 40960 \
  --max-num-batched-tokens 40960 \
  --max-num-seqs 1 \
  --attention-backend FLASH_ATTN \
  --mm-encoder-attn-backend FLASH_ATTN \
  --mm-processor-kwargs '{"downsample_mode":"4x"}' \
  --trust-request-mm-kwargs \
  --disable-custom-all-reduce \
  --compilation-config '{"pass_config":{"fuse_allreduce_rms":false}}' \
  --speculative-config '{"method":"dspark","model":"openbmb/MiniCPM-V-4.7-35B-A3B-DSpark","num_speculative_tokens":15,"enable_adaptive_verification":false}' \
  --default-chat-template-kwargs '{"enable_thinking":false}'

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.

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

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