DeepSeek V4 Pro 1.6T
By DeepSeek · China
Updated 2026-07-13
Overview
DeepSeek's frontier MoE: 1.6T total / 49B active params, MIT-licensed, 1M context, with CSA+HCA hybrid attention and three reasoning modes. The absolute open-weight ceiling as of April 2026.
When to pick this model
- Research labs benchmarking against closed frontier models
- Workloads where MIT licensing on frontier quality is the goal
- Million-token context tasks (whole codebases, books, archives)
- Multi-mode reasoning workflows (Non / High / Max)
- Datacenter deployments that can absorb ~1 TB VRAM
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 960 GB |
| Q5_K_M | 1150 GB |
| Q8_0 | 1700 GB |
| FP16 (no quantization) | 3200 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, DeepSeek V4 Pro 1.6T is server-class even at Q4_K_M (960 GB). Stepping up to Q8_0 nearly doubles the footprint to 1700 GB, and unquantized FP16 weights take 3200 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, DeepSeek V4 Pro 1.6T needs roughly 1100 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 0.5 tokens/sec on entry-level GPUs, on the order of 2 tokens/sec on a mid-range card, and up to 8 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches DeepSeek V4 Pro 1.6T to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for DeepSeek V4 Pro 1.6T |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 960 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 960 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 960 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 960 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 960 GB at Q4_K_M |
Which GPU should you buy to run DeepSeek V4 Pro 1.6T?
To run DeepSeek V4 Pro 1.6T locally at Q4, you need ~960 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Strengths
- The most capable open-weight model available, period
- MIT license at frontier scale
- 1M context window
- Three configurable thinking modes (Non / High / Max)
- Hybrid CSA+HCA attention for efficient long-context
Limitations
- 960+ GB VRAM in Q4 — server farm only
- No community quantizations yet at release
- Three-mode reasoning adds inference complexity
- 32T+ token pretraining means very high training carbon footprint
Typical workloads
In our catalog grid, DeepSeek V4 Pro 1.6T is filed under Open Frontier, Reasoning, 1M Long Context — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; multilingual workloads.
The 976k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE 1.6T/49B active · CSA+HCA hybrid attention · mHC · Muon optimizer · mixed FP4+FP8
Training: 32T+ tokens pre-training.
The new open-weight ceiling. If you have the hardware, nothing else comes close.
Quick start
# HuggingFace : deepseek-ai/DeepSeek-V4-ProOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does DeepSeek V4 Pro 1.6T need?
At the recommended Q4_K_M quantization, DeepSeek V4 Pro 1.6T needs about 960 GB of VRAM. Q8_0 takes 1700 GB, and unquantized FP16 weights take 3200 GB.
Can DeepSeek V4 Pro 1.6T run without a GPU?
Yes — with roughly 1100 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does DeepSeek V4 Pro 1.6T support?
DeepSeek V4 Pro 1.6T supports a 976k-token context window (1,000,000 tokens).
Can I use DeepSeek V4 Pro 1.6T commercially?
Yes. DeepSeek V4 Pro 1.6T is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is DeepSeek V4 Pro 1.6T on consumer hardware?
Our compatibility engine estimates on the order of 2 tokens/sec on a mid-range GPU and up to 8 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of DeepSeek V4 Pro 1.6T should I download first?
Start with Q4_K_M (960 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.