deepseek-ai / text-generation updated 8 months ago

DeepSeek-V3.2-Exp

Name: DeepSeek-V3.2-Exp
Author: deepseek-ai

We are excited to announce the official release of DeepSeek-V3.2-Exp, an experimental version of our model. As an intermediate step toward our next-generation architecture, V3.2-Exp builds upon V3.1-Terminus by introducing DeepSeek Sparse Attention—a sparse attention mechanism designed to explore and validate optimizations for training and inference efficiency in long-context scenarios.

Params

685.4 B

Context

163,840

Downloads 30d

252 K

Likes

993

Commercial use: allowed mit Not gated SAFETENSORS View on Hugging Face ↗

Download history

daily snapshots · 10 days

288 K252 K

Jul 28Jul 31Aug 3Aug 6

Can you run it?

Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.

File	Quant	Size	Est. VRAM	Verdict on RTX 4090 · 24 GB
model.safetensors	f8_e4m3	689.5 GB	861.7 GB	❌ Won’t fit
model.safetensors (bf16, full)	bf16 + 160K ctx	689.5 GB	2,815.1 GB	❌ Won’t fit

Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.

Run it

copy-paste, exact tags checked against the Hub

~ · curl · api/v1

$ curl -s https://aimodelscomparison.com/api/v1/models/deepseek-v3-2-exp
{
  "hf_id": "deepseek-ai/DeepSeek-V3.2-Exp",
  "params_b": 685.40,
  "context_length": 163840,
  "license": { "id": "mit", "commercial": "yes" },
  "downloads_30d": 251826,
  "vram_estimates": [
    { "quant": "f8_e4m3", "gb": 861.7 }
  ],
  "updated_at": "2026-07-28T18:06:54Z"
}

est. VRAM —on RTX 4090 · 24 GBJSON API →

Specifications

Architecture: DeepseekV32ForCausalLM
Parameters: 685.4 B
Tensor type: F8_E4M3
Context length: 163,840
Vocabulary: 129,280
Layers / heads: 61 / 128
Licence: mit
First seen on the Hub: 2025-09-29
Training datasets: undisclosed
Added to our catalog: 2026-07-28

Compare with

DeepSeek-R1 9.9 M GLM-5.2-FP8 2.7 M GLM-5.2 2.2 M DeepSeek-V3.2 1.2 M DeepSeek-V3 1.2 M GLM-5-FP8 1.2 M

Appears in

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