DeepSeek V4 Pro 0813 1.7T
By DeepSeek · China
Updated 2026-08-28
Overview
DeepSeek's frontier-scale MoE (~1.7T total params) released August 12, 2026, with a 1M-token context and MIT license — built for multi-GPU clusters, not single-box deployment.
When to pick this model
- Frontier-class reasoning workloads where you need the largest open model available
- Multi-GPU/multi-node inference clusters with ~986GB+ VRAM at Q4
- Long-document or long-codebase analysis needing the full 1M-token context
- Organizations that need MIT licensing at frontier scale
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 986 GB |
| Q5_K_M | 1207 GB |
| Q8_0 | 1819 GB |
| FP16 (no quantization) | 3400 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 0813 1.7T is server-class even at Q4_K_M (986 GB). Stepping up to Q8_0 nearly doubles the footprint to 1819 GB, and unquantized FP16 weights take 3400 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, DeepSeek V4 Pro 0813 1.7T needs roughly 2210 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 1.5 tokens/sec on entry-level GPUs, on the order of 2.5 tokens/sec on a mid-range card, and up to 5 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 0813 1.7T 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 0813 1.7T |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 986 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 986 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 986 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 986 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 986 GB at Q4_K_M |
Which GPU should you buy to run DeepSeek V4 Pro 0813 1.7T?
To run DeepSeek V4 Pro 0813 1.7T locally at Q4, you need ~986 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Strengths
- Frontier-scale open model at ~1.7T total parameters
- 1M-token context window
- MIT license even at this scale
- MoE architecture keeps active parameters (and inference cost) far below the total count
Limitations
- ~986GB VRAM at Q4 requires a serious multi-GPU cluster
- Local throughput is very low (~5 tok/s at Q4)
- No official Ollama tag — HuggingFace install only
Typical workloads
In our catalog grid, DeepSeek V4 Pro 0813 1.7T is filed under Open Frontier, Reasoning, Long Context 1M — 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.
The 1024k-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, ~1.7T total parameters · 1,048,576-token context window (1M)
Training: Build dated 0813 of DeepSeek V4 Pro, succeeding the previous build. Training details not published.
An open frontier-scale MoE with a 1M-token context and MIT license — the largest option here, but strictly a data-center play.
Quick start
# HuggingFace : deepseek-ai/DeepSeek-V4-Pro-0813Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
Similar models worth comparing
Frequently asked questions
How much VRAM does DeepSeek V4 Pro 0813 1.7T need?
At the recommended Q4_K_M quantization, DeepSeek V4 Pro 0813 1.7T needs about 986 GB of VRAM. Q8_0 takes 1819 GB, and unquantized FP16 weights take 3400 GB.
Can DeepSeek V4 Pro 0813 1.7T run without a GPU?
Yes — with roughly 2210 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 0813 1.7T support?
DeepSeek V4 Pro 0813 1.7T supports a 1024k-token context window (1,048,576 tokens).
Can I use DeepSeek V4 Pro 0813 1.7T commercially?
Yes. DeepSeek V4 Pro 0813 1.7T is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is DeepSeek V4 Pro 0813 1.7T on consumer hardware?
Our compatibility engine estimates on the order of 2.5 tokens/sec on a mid-range GPU and up to 5 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of DeepSeek V4 Pro 0813 1.7T should I download first?
Start with Q4_K_M (986 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.
Is DeepSeek V4 Pro 0813 1.7T the right pick for you?