Model reference · open weights
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.
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 by | openbmb |
|---|---|
| Released | 2026-10-08 |
| Parameters | 389M |
| VRAM | 427 MB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB | 124 | 31 | all 256K | 11.6 GB |
| RTX 4060 Ti 16 GB | 169 | 42 | all 256K | 15.4 GB |
| RTX 3090 24 GB | 265 | 66 | all 256K | 23.4 GB |
| RTX 4090 24 GB | 264 | 66 | all 256K | 23.4 GB |
| RTX 5090 32 GB | 355 | 88 | all 256K | 31.0 GB |
| L40S 48 GB | 510 | 127 | all 256K | 44.0 GB |
| A100 80 GB | 918 | 229 | all 256K | 78.2 GB |
| H100 80 GB | 857 | 214 | all 256K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 1000+ | 261 | all 256K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 1000+ | 301 | all 256K | 107 GB |
| H200 141 GB | 1000+ | 392 | all 256K | 138 GB |
| B200 180 GB | 1000+ | 506 | all 256K | 176 GB |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 1.2 GB | 1.5 GB |
| 5 | 1.6 GB | 2.8 GB |
| 8 | 1.8 GB | 3.8 GB |
| 16 | 2.5 GB | 6.5 GB |
| 32 | 3.8 GB | 11.9 GB |
| 64 | 6.5 GB | 22.6 GB |
One card, with vLLM's small-card settings.
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| MiniCPM-V-4.7-35B-A3B-DSpark ↗ | 389M | BF16 | 778 MB | RTX 3060 12 GB |
| MiniCPM-V-4.7-1B-DSpark (above) ↗ | 214M | BF16 | 427 MB | RTX 3060 12 GB |
| MiniCPM-V-4.7-35B-A3B-DSpark-gguf ↗ 11 builds: Q4_0 230 MB … F16 789 MB |
GGUF | 237 MB | RTX 3060 12 GB |
From 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.
| Property | Value |
|---|---|
| Target model | MiniCPM-V-4.7-35B-A3B |
| Draft layers | 5 |
| Draft parameters | 389,223,169 |
| Draft tokens per forward pass | 15 |
| Target hidden-state layers | [3, 11, 19, 27, 35] |
| Checkpoint precision | BF16 |
Training responses were generated by MiniCPM-V-4.7-35B-A3B from a mixture of Vision & Text prompts.
| Setting | Value |
|---|---|
| Training sequences | 2M |
| Epochs | 6 |
| Optimizer | AdamW |
| Objective | CE + L1 + confidence loss |
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.
| Domain | Accept Length |
|---|---|
| Vision | |
| MMMU | 5.098 |
| MathVista | 6.126 |
| CharXiv_reasoning_val | 5.479 |
| OCRBench | 5.407 |
| Text | |
| Math | 6.687 |
| Code | 7.387 |
| General | 3.501 |
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}'
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.