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DeepSeek V4 Flash

NVIDIA
DeepSeek V4 Flash is a large-scale Mixture-of-Experts model optimized for ultra-long context reasoning and efficient inference. It features 284B total parameters with approximately 13B activated, using 256 routed experts with 6 selected per token across a 43-layer architecture with 4,096 hidden size and 64 attention heads. Built with hybrid CSA + HCA attention and manifold-constrained hyper-connections, it supports up to a 1M token context window. With FP4 and FP8 mixed precision and strong agentic tool-calling capabilities, it is designed for scalable, high-efficiency reasoning and multi-domain workloads.
TypeMoE LLM
CapabilitiesText Generation, Instruction Following, Reasoning, Mathematical Reasoning+5 more
Release Date24 April, 2026
Links
Blog|HF Model Card
LicenseMIT

Inference Instructions

Deploy and run this model on NVIDIA B200 GPUs using the command below. Copy the command to get started with inference.

CONSOLE
docker run -it --rm 
 --runtime=nvidia 
 --gpus all 
 --ipc=host 
 --shm-size=128g 
 -p 8000:8000 
 -v ~/.cache/huggingface:/root/.cache/huggingface 
 -e HF_TOKEN='YOUR_HF_TOKEN' 
 vllm/vllm-openai:v0.20.0 
 deepseek-ai/DeepSeek-V4-Flash 
 --attention_config.use_fp4_indexer_cache=True 
 --kv-cache-dtype fp8 
 --block-size 256 
 --tensor-parallel-size 4 
 --enable-expert-parallel 
 --max-model-len auto 
  --max-num-batched-tokens 65536 
 --compilation-config '{\"cudagraph_mode\":\"FULL_AND_PIECEWISE\", \"custom_ops\":[\"all\"]}' 
 --gpu-memory-utilization 0.90 
 --tool-call-parser deepseek_v4 
 --reasoning-parser deepseek_v4 
 --tokenizer-mode deepseek_v4 
 --enable-auto-tool-choice 
 --max-num-seqs 1024 
 --trust-remote-code

Model Benchmarks

Each model was tested with a fixed input size and total token volume while increasing concurrency to measure serving performance under load.

ITL vs Concurrency

Time to First Token

Throughput Scaling

Total Tokens/sec vs Avg TTFT

Vultr Cloud GPU

NVIDIA HGX B200

Deploy NVIDIA B200 on Vultr Cloud GPU

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