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-4.7-1B-DSpark (BF16) weighs 427 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
VRAM427 MB for the weights

What it runs on

Memory and cards for MiniCPM-V-4.7-1B-DSpark (BF16)

427 MBweights, file size
10 MBcache per 1K tokens
730 MBruntime overhead, at least
262,144 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB12431all 256K11.6 GB
RTX 4060 Ti 16 GB16942all 256K15.4 GB
RTX 3090 24 GB26566all 256K23.4 GB
RTX 4090 24 GB26466all 256K23.4 GB
RTX 5090 32 GB35588all 256K31.0 GB
L40S 48 GB510127all 256K44.0 GB
A100 80 GB918229all 256K78.2 GB
H100 80 GB857214all 256K78.1 GB
RTX PRO 6000 Blackwell 96 GB1000+261all 256K93.8 GB
DGX Spark (GB10) 128 GB unified1000+301all 256K107 GB
H200 141 GB1000+392all 256K138 GB
B200 180 GB1000+506all 256K176 GB
Memory needed at each load
Requests at once8K tokens each32K tokens each
11.2 GB1.5 GB
51.6 GB2.8 GB
81.8 GB3.8 GB
162.5 GB6.5 GB
323.8 GB11.9 GB
646.5 GB22.6 GB

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

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 (above) ↗ 214MBF16 427 MBRTX 3060 12 GB
MiniCPM-V-4.7-35B-A3B-DSpark-gguf ↗
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.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms