DeepSeek V4 (1T MoE, Multimodal)

Deploy DeepSeek V4 — the trillion-parameter multimodal open-weight model — on Clore.ai GPU servers

Status (March 4, 2026): DeepSeek V4 release is imminent — expected first week of March 2026. This guide covers setup using vLLM/Ollama once weights drop on HuggingFace. Check huggingface.co/deepseek-ai for the latest release.

DeepSeek V4 is the most anticipated open-weight model of early 2026 — a ~1 trillion parameter multimodal MoE from DeepSeek AI, trained on NVIDIA's latest chips and optimized for Huawei Ascend hardware. With ~32B active parameters per token, it delivers frontier-class performance at a fraction of the compute cost.

Key Specs

Property	Value
Total Parameters	~1 Trillion (MoE)
Active Parameters	~32B per forward pass
Context Window	1M tokens
Modalities	Text + Image + Video
License	Expected MIT (like V3)
Benchmark	Expected to top open-source leaderboards

Why DeepSeek V4?

• #1 open-weight model — designed to surpass V3 and rival GPT-4.5/Claude Opus

• Multimodal — natively handles text, image, and video inputs

• 1M context — long-document RAG, entire codebases in context

• MIT license — commercial use allowed, no restrictions

• Massive efficiency — only 32B active params despite 1T total

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Requirements

Component	Minimum	Recommended
GPU VRAM	2× RTX 4090 (48GB) for Q4	4× A100 80GB for FP16
RAM	64GB	128GB
Disk	500GB (quantized)	2TB (FP16)
CUDA	12.4+	12.6+

Large model alert: DeepSeek V4 at FP16 requires ~2TB of VRAM across multiple A100/H100s. For practical single/dual-node use, wait for GGUF Q4 quantization (expected within days of release). Q4_K_M at ~1T params ≈ ~500GB — use multi-node or 8-bit quant on 4× RTX 4090.

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Option A — Quantized via Ollama (Easiest, once available)

Ollama will add DeepSeek V4 models within hours of weights dropping.

version: "3.8"
services:
  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    environment:
      - OLLAMA_KEEP_ALIVE=24h
      - OLLAMA_MAX_LOADED_MODELS=1

volumes:
  ollama_data:

# Pull and run DeepSeek V4 (update tag once released)
docker exec ollama ollama pull deepseek-v4:32b-q4_K_M
docker exec ollama ollama run deepseek-v4:32b-q4_K_M

# Or via Open WebUI for a full chat interface
docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  ghcr.io/open-webui/open-webui:main

---

Option B — vLLM (Production API, high throughput)

version: "3.8"
services:
  vllm:
    image: vllm/vllm-openai:latest
    ports:
      - "8000:8000"
    volumes:
      - hf_cache:/root/.cache/huggingface
    command: >
      --model deepseek-ai/DeepSeek-V4
      --tensor-parallel-size 4
      --max-model-len 32768
      --dtype bfloat16
      --gpu-memory-utilization 0.92
      --served-model-name deepseek-v4
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    shm_size: "16gb"

volumes:
  hf_cache:

# Test the API
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4",
    "messages": [{"role": "user", "content": "Explain quantum entanglement simply"}],
    "max_tokens": 512
  }'

---

Option C — llama.cpp (CPU+GPU, quantized)

# Once GGUF files are available on HuggingFace
docker run --gpus all -it --rm \
  -p 8080:8080 \
  -v $(pwd)/models:/models \
  ghcr.io/ggerganov/llama.cpp:server-cuda \
  -m /models/deepseek-v4-q4_k_m.gguf \
  --n-gpu-layers 80 \
  --threads 8 \
  --ctx-size 8192 \
  --port 8080 \
  --host 0.0.0.0

---

GPU Recommendations on Clore.ai

Setup	VRAM	Expected Performance	Clore.ai Cost
2× RTX 4090	48GB	Q4 quantized, ~15 tok/s	~$4–5/day
4× RTX 4090	96GB	Q5/Q8 quantized, ~25 tok/s	~$8–10/day
4× A100 80GB	320GB	BF16 MoE sharding, fast	~$15–20/day
8× H100 80GB	640GB	Full FP16, maximum speed	~$50+/day

Best value on Clore.ai: Rent 2× RTX 4090 (available from ~$4/day) for Q4 quantized DeepSeek V4. Expect 10–20 tokens/second — perfect for personal use and development.

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Clore.ai Port Forwarding

Add these to your Clore.ai container port configuration:

Port	Service
11434	Ollama API
8000	vLLM OpenAI-compatible API
8080	llama.cpp server / Open WebUI
3000	Open WebUI chat interface

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Performance Tips

1. Use Q4_K_M quantization for best quality/VRAM tradeoff — still beats most 70B models

2. Enable flash attention: add --enable-chunked-prefill in vLLM for long contexts

3. Tensor parallelism: vLLM's --tensor-parallel-size N across N GPUs is seamless

4. Context length: Start with 8192 ctx on 2× 4090, increase if VRAM allows

5. BF16 > FP16 for MoE models — less precision loss on sparse activations

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What to Expect

Based on DeepSeek V3 patterns and pre-release benchmarks:

• Coding: Expected top-tier on SWE-bench (rivaling Claude 3.7 Sonnet)

• Math/Reasoning: MATH-500 and AIME scores above all open-weight predecessors

• Multimodal: Image and video understanding comparable to GPT-4V

• Long context: 1M token window for entire codebase analysis

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Links

• HuggingFace: huggingface.co/deepseek-ai (weights will appear here)

• GitHub: github.com/deepseek-ai

• DeepSeek V3 guide (current): DeepSeek-V3

• DeepSeek-R1 guide: DeepSeek-R1

• Clore.ai Marketplace: clore.ai/marketplace