dots-studio/dots3-note-prev

Overview

dots3-note-prev is a native multimodal Mixture-of-Experts model supporting text, image, video, and audio inputs through one model architecture. It is published as a full BF16 checkpoint (dots-studio/dots3-note-prev) and a native FP8 checkpoint (dots-studio/dots3-note-prev-fp8). Its language backbone has 256 routed experts (top-8) plus a shared expert and mixes DSA and SWA layers. Both releases expose MTP speculative decoding.

The model configuration supports up to 524,288 tokens. The commands below use a validated 262,144-token serving limit and 8,192-token chunked-prefill budget so the remaining GPU memory can be used for KV cache. Increase --max-model-len only after checking the KV-cache budget for the target workload.

Prerequisites

• 8 NVIDIA GPUs with at least 80 GB each for the native FP8 checkpoint.
• The BF16 checkpoint contains about 576.9 GB of indexed weights and needs additional memory for the runtime and KV cache; the recipe budgets 692 GB aggregate VRAM.
• A recent CUDA environment. Native FP8 uses DeepGEMM, while unquantized BF16 uses the Triton MoE backend.
• vLLM 0.28.0 or newer, which includes native dots3-note-prev support.

Launch commands

The examples expose an OpenAI-compatible endpoint on all network interfaces. Remove --host 0.0.0.0 if the service should only be reachable locally. The general strategy examples use the FP8 checkpoint. BF16 needs a different MoE backend and a larger memory budget, so use the explicit BF16 command below instead of only replacing the model ID.

BF16 tensor parallel + expert parallel (TP=8, EP=8)

This command serves the full multimodal BF16 checkpoint. Triton is required because DeepGEMM does not support unquantized MoE weights. A 0.95 memory-utilization target leaves enough KV-cache capacity for the 262,144-token serving limit after profiling the multimodal encoders. If the target hardware cannot reserve that much memory, lower --max-model-len or add --language-model-only for text workloads.

vllm serve dots-studio/dots3-note-prev \
  --served-model-name dots3-note-prev \
  --host 0.0.0.0 \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --gpu-memory-utilization 0.95 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --block-size 64 \
  --mm-processor-cache-gb 32 \
  --moe-backend triton

Tensor parallel (TP=8)

TP shards both dense and expert weights across all eight GPUs. It is the simplest deployment, but it does not enable expert parallelism.

vllm serve dots-studio/dots3-note-prev-fp8 \
  --served-model-name dots3-note-prev \
  --host 0.0.0.0 \
  --tensor-parallel-size 8 \
  --gpu-memory-utilization 0.9 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --block-size 64 \
  --mm-processor-cache-gb 32 \
  --moe-backend deep_gemm

Tensor parallel + expert parallel (TP=8, EP=8)

This is the recommended single-node configuration. Dense layers use TP while MoE experts are distributed across the eight expert-parallel ranks.

vllm serve dots-studio/dots3-note-prev-fp8 \
  --served-model-name dots3-note-prev \
  --host 0.0.0.0 \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --gpu-memory-utilization 0.9 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --block-size 64 \
  --mm-processor-cache-gb 32 \
  --moe-backend deep_gemm

Data parallel + expert parallel (DP=8, EP=8)

DP+EP is intended for throughput-oriented serving. Each DP rank owns a replica of the non-expert weights, while routed experts are distributed across all eight ranks. It therefore needs more GPU memory than TP+EP.

vllm serve dots-studio/dots3-note-prev-fp8 \
  --served-model-name dots3-note-prev \
  --host 0.0.0.0 \
  --data-parallel-size 8 \
  --enable-expert-parallel \
  --gpu-memory-utilization 0.9 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --block-size 64 \
  --mm-processor-cache-gb 32 \
  --moe-backend deep_gemm

Language-model-only serving

The checkpoint keeps a single multimodal architecture and model type. For pure-text serving, add --language-model-only; a separate text checkpoint is not required. The option can be combined with TP, TP+EP, or DP+EP.

vllm serve dots-studio/dots3-note-prev-fp8 \
  --served-model-name dots3-note-prev \
  --host 0.0.0.0 \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --language-model-only \
  --gpu-memory-utilization 0.9 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --block-size 64 \
  --mm-processor-cache-gb 32 \
  --moe-backend deep_gemm

MTP speculative decoding

Add the speculative configuration to any of the deployments above. The current checkpoints expose an MTP prediction head that vLLM reuses for drafting without loading a separate draft model. The example uses TP+EP with the native FP8 checkpoint.

vllm serve dots-studio/dots3-note-prev-fp8 \
  --served-model-name dots3-note-prev \
  --host 0.0.0.0 \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}' \
  --gpu-memory-utilization 0.9 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --block-size 64 \
  --mm-processor-cache-gb 32 \
  --moe-backend deep_gemm

Tool calling

dots3-note-prev emits tool calls inside <dots_function_call> XML wrappers. Enable automatic tool choice with the native dots parser. These options can be combined with any parallel strategy, --language-model-only, and MTP speculative decoding.

vllm serve dots-studio/dots3-note-prev-fp8 \
  --served-model-name dots3-note-prev \
  --host 0.0.0.0 \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --enable-auto-tool-choice \
  --tool-call-parser dots \
  --gpu-memory-utilization 0.9 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --block-size 64 \
  --mm-processor-cache-gb 32 \
  --moe-backend deep_gemm

Client usage

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "dots3-note-prev",
    "messages": [{"role": "user", "content": "Explain sparse attention briefly."}],
    "temperature": 0.7
  }'

Tuning notes

• --max-num-batched-tokens 8192 is the chunked-prefill budget, not the maximum request length. Increase it only when prefill throughput is more important than inter-token latency and enough temporary workspace is available.
• DP+EP replicates the non-expert portion on every DP rank. Prefer TP+EP when memory capacity or long-context KV cache is the priority.
• --language-model-only skips multimodal encoder loading but does not change the checkpoint's model type or architecture.
• Full multimodal BF16 serving at 262,144 tokens may not have enough KV-cache memory with --gpu-memory-utilization 0.9; the BF16 variant uses 0.95. Lower --max-model-len instead when the extra memory cannot be reserved.
• DeepGEMM is specific to the native FP8 checkpoint in this recipe. BF16 is unquantized and must use --moe-backend triton.
• MTP changes decode scheduling and memory use. Benchmark it against non-speculative decoding with the same prompt/output distribution before production deployment.
• --tool-call-parser dots supports the model's XML invoke/parameter format and its JSON fallback format, including multiple tool calls in one response.

References

• dots3-note-prev FP8 on Hugging Face
• dots3-note-prev BF16 on Hugging Face
• vLLM dots3-note-prev support PR
• vLLM parallelism and scaling guide