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.
DeepSeek V3.2 Speciale runs on a 512 GB+ Mac or a multi-GPU server
Best local pick → Mac Studio (M3 Ultra, 512GB) · Q4_K_M · 412.7 GB · ~18 tok/s
Macs from 512 GB (Q4_K_M · ~18 tok/s)
MIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run DeepSeek V3.2 Speciale
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve deepseek-ai/DeepSeek-V3.2-Speciale --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: DeepSeek-V3.2-Speciale-Q4_K_M.gguf411.24 GBubergarm
Ways to runOther ways
each opens step-by-stepRun in cloud
Rent 6× H100 SXM · Q4_K_M
- Estimated hourly price
- ~$13.21/hr
- est., whole server
- GPUs
- 6 GPUs
- 480 GB of VRAM
How Much VRAM Does DeepSeek V3.2 Speciale Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 295.2 GB | 293.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on 5× H100 SXM 80 GB | |
| 338.0 GB | 336.7 GB | 3-bit medium — 3-bit medium quantization | Runs on 5× H100 SXM 80 GB | |
| 415.1 GB | 413.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 6× H100 SXM 80 GB | |
| 492.2 GB | 491 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 7× H100 SXM 80 GB | |
| 569.3 GB | 568.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 8× H100 SXM 80 GB | |
| 689.3 GB | 688 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 6× H200 SXM 141 GB | |
| 1374.7 GB | 1373.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on 8× B200 192 GB |
Selected Q4_K_M
4-bit medium quantization412.7GBat 16K context
- Weights
- 411.2 GB
- KV cache
- 1.2 GB
- Overhead
- 0.3 GB
Server-class: ~412.7 GB at 16K context · about 6× H100 SXM 80 GB
Which Hardware 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 7.9 GB, bringing total usage to 423.1 GB. No consumer GPU has enough memory. 1 device with unified memory can run DeepSeek V3.2 Speciale, including Mac Studio (M3 Ultra, 512GB).
Needs server-class hardware
DeepSeek V3.2 Speciale runs on a 512 GB+ Mac or a multi-GPU server · Q4_K_M needs ~412.7 GB
| Hardware | Quant | Memory | Speed | Context | Hardware page | |
|---|---|---|---|---|---|---|
| Mac 512 GB · Mac Studio (M3 Ultra, 512GB) | Q4_K_M | 412.7 GB | ~18 tok/s | 160K ctx | Mac Studio → | Q4_K_M · 412.7 GB · ~18 tok/s · 160K ctx |
| 6× H100 SXM 80 GB · 480 GB | Q4_K_M | 412.7 GB | — | 160K ctx | H100 SXM → | Q4_K_M · 412.7 GB · 160K ctx |
| 4× H200 SXM 141 GB · 564 GB | Q4_K_M | 412.7 GB | — | 160K ctx | H200 SXM → | Q4_K_M · 412.7 GB · 160K ctx |
| 3× B200 192 GB · 576 GB | Q4_K_M | 412.7 GB | — | 160K ctx | B200 → | Q4_K_M · 412.7 GB · 160K ctx |
Also: Mac Studio (M3 Ultra, 512GB) · NVIDIA DGX A100 640GB · NVIDIA DGX H100
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 256 GB: too small for this model (needs ≥ 292.8 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is DeepSeek V3.2 Speciale?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 2 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- SimpleBench
Epoch AI · Dec 2025Independent
Independent52.6%
#7/25 open
- WeirdML
Epoch AI · Dec 2025Independent
Independent46.7%
#13/47 open
Alternatives to DeepSeek V3.2 Speciale
DeepSeek V3 family · 9 models →Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| DeepSeek R1 0528DeepSeek | 103.7 | 412.2 GB | —needs > 24 GB | same size |
| Mistral Large 3 675B Instruct 2512Mistral AI | 89.6 | 454.4 GB | —needs > 24 GB | same size |
| Cogito 671B V2.1deepcogito | — | 404.1 GB | —needs > 24 GB | same size |
| A.X K2skt | — | 444 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- DeepSeek
- Family
- DeepSeek V3
- Parameters
- 685.4B
- Active parameters
- 41B per token
- Architecture
- DeepseekV32ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2025-11-28
- License
- MIT
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 7.9 GB (423.1 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 11.9 GB (at full 164K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run DeepSeek V3.2 Speciale on a Mac?
Yes — Mac Studio (M3 Ultra, 512GB) and above can run DeepSeek V3.2 Speciale. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 415.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run DeepSeek V3.2 Speciale locally?
Yes — DeepSeek V3.2 Speciale can run locally on consumer hardware. 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?
1 device with unified memory can run DeepSeek V3.2 Speciale at Q4_K_M (415.1 GB), including Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.