DeepSeek V3.2 Speciale — Hardware Requirements & GPU Compatibility
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DeepSeek-V3.2-Speciale is DeepSeek's high-compute reasoning variant of DeepSeek-V3.2, a mixture-of-experts model with roughly 41 billion active parameters out of about 685 billion total, fine-tuned from DeepSeek-V3.2-Exp-Base. It uses DeepSeek Sparse Attention (DSA), an efficient attention mechanism aimed at long-context scenarios, together with heavy reinforcement-learning post-training; the card reports gold-medal-level performance at the 2025 International Mathematical Olympiad and International Olympiad in Informatics, and claims it surpasses GPT-5 with reasoning on par with Gemini 3.0 Pro. Unlike the standard DeepSeek-V3.2 checkpoint, Speciale is dedicated purely to deep reasoning and does not support tool-calling. At this scale it requires a multi-GPU server cluster to run even quantized. Context length is 163,840 tokens. It is released under the MIT license, permitting unrestricted commercial and research use, and was published in November 2025.
4.3K downloads
726 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek V3
- Parameters
- 685.4B
- Architecture
- DeepseekV32ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2025-11-28
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does DeepSeek V3.2 Speciale Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 295.2 GB | 578.1 GB | 291.29 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 338.0 GB | 621.0 GB | 334.13 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 415.1 GB | 698.1 GB | 411.24 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 492.2 GB | 775.2 GB | 488.35 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 569.3 GB | 852.3 GB | 565.45 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 689.3 GB | 972.3 GB | 685.40 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 1374.7 GB | 1657.7 GB | 1370.79 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run DeepSeek V3.2 Speciale?
Q4_K_M · 415.1 GBDeepSeek V3.2 Speciale (Q4_K_M) requires 415.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 540+ GB is recommended. Using the full 164K context window can add up to 283.0 GB, bringing total usage to 698.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run DeepSeek V3.2 Speciale?
Q4_K_M · 415.1 GB2 devices with unified memory can run DeepSeek V3.2 Speciale, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does DeepSeek V3.2 Speciale need?
DeepSeek V3.2 Speciale requires 415.1 GB of VRAM at Q4_K_M, or 1374.7 GB at BF16. Full 164K context adds up to 283.0 GB (698.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 685.4B × 4.8 bits ÷ 8 = 411.2 GB
KV Cache + Overhead ≈ 3.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 286.9 GB (at full 164K context)
VRAM usage by quantization
Q4_K_M415.1 GBQ4_K_M + full context698.1 GB- Can NVIDIA GeForce RTX 5090 run DeepSeek V3.2 Speciale?
No — DeepSeek V3.2 Speciale requires at least 295.2 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for DeepSeek V3.2 Speciale?
For DeepSeek V3.2 Speciale, Q4_K_M (415.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (492.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 295.2 GB.
VRAM requirement by quantization
Q2_K295.2 GBQ4_K_M ★415.1 GBQ5_K_M492.2 GBQ6_K569.3 GBQ8_0689.3 GBBF161374.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek V3.2 Speciale on a Mac?
DeepSeek V3.2 Speciale requires at least 295.2 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run DeepSeek V3.2 Speciale locally?
Yes — DeepSeek V3.2 Speciale can run locally on consumer hardware. At Q4_K_M quantization it needs 415.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of DeepSeek V3.2 Speciale?
At Q4_K_M, the download is about 411.24 GB. The full-precision BF16 version is 1370.79 GB. The smallest option (Q2_K) is 291.29 GB.
- Which GPUs can run DeepSeek V3.2 Speciale?
No single consumer GPU has enough VRAM to run DeepSeek V3.2 Speciale at Q4_K_M (415.1 GB). Multi-GPU or professional hardware is required.
- Which devices can run DeepSeek V3.2 Speciale?
3 devices with unified memory can run DeepSeek V3.2 Speciale at Q4_K_M (415.1 GB), including Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.