DeepSeek v2 — Hardware Requirements & GPU Compatibility
Chat
DeepSeek-V2 is DeepSeek's base pretrained Mixture-of-Experts language model, not instruction-tuned, comprising about 235.7 billion total parameters with roughly 21.4 billion activated per token. It was pretrained on 8.1 trillion tokens and built for economical training and efficient inference, cutting training costs and KV-cache size sharply compared with DeepSeek's earlier dense 67B model while boosting generation throughput. Instruction-tuned SFT and RL chat variants were released separately as DeepSeek-V2-Chat. Despite the large total parameter count, its MoE design activates only a fraction per token, but it still needs a multi-GPU server to run at full precision. Context length is 163,840 tokens. It is released under a custom DeepSeek Model License that permits free use, including commercial use, subject to acceptable-use restrictions in the license agreement. It was published in April 2024, ahead of the DeepSeek-V2-Chat and later DeepSeek-V3 models.
39.7K downloads
336 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek V2
- Parameters
- 235.7B
- Architecture
- DeepseekV2ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2024-04-22
- License
- Other
Get Started
HuggingFace
How Much VRAM Does DeepSeek v2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 103.0 GB | 301.8 GB | 100.19 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 117.7 GB | 316.6 GB | 114.92 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 144.3 GB | 343.1 GB | 141.44 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 170.8 GB | 369.6 GB | 167.97 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 197.3 GB | 396.1 GB | 194.49 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 238.6 GB | 437.4 GB | 235.74 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 474.3 GB | 673.1 GB | 471.48 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 v2?
Q4_K_M · 144.3 GBDeepSeek v2 (Q4_K_M) requires 144.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 188+ GB is recommended. Using the full 164K context window can add up to 198.8 GB, bringing total usage to 343.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run DeepSeek v2?
Q4_K_M · 144.3 GB6 devices with unified memory can run DeepSeek v2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 256GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download DeepSeek v2
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does DeepSeek v2 need?
DeepSeek v2 requires 144.3 GB of VRAM at Q4_K_M, or 474.3 GB at BF16. Full 164K context adds up to 198.8 GB (343.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 235.7B × 4.8 bits ÷ 8 = 141.4 GB
KV Cache + Overhead ≈ 2.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 201.7 GB (at full 164K context)
VRAM usage by quantization
Q4_K_M144.3 GBQ4_K_M + full context343.1 GB- Can NVIDIA GeForce RTX 5090 run DeepSeek v2?
No — DeepSeek v2 requires at least 103.0 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for DeepSeek v2?
For DeepSeek v2, Q4_K_M (144.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (170.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 103.0 GB.
VRAM requirement by quantization
Q2_K103.0 GBQ4_K_M ★144.3 GBQ5_K_M170.8 GBQ6_K197.3 GBQ8_0238.6 GBBF16474.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek v2 on a Mac?
DeepSeek v2 requires at least 103.0 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 v2 locally?
Yes — DeepSeek v2 can run locally on consumer hardware. At Q4_K_M quantization it needs 144.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek v2?
At Q4_K_M, DeepSeek v2 can reach ~68 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 144.3 × 0.65 = ~172 tok/s
Estimated speed at Q4_K_M (144.3 GB)
~172 tok/s~172 tok/s~68 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of DeepSeek v2?
At Q4_K_M, the download is about 141.44 GB. The full-precision BF16 version is 471.48 GB. The smallest option (Q2_K) is 100.19 GB.
- Which GPUs can run DeepSeek v2?
No single consumer GPU has enough VRAM to run DeepSeek v2 at Q4_K_M (144.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run DeepSeek v2?
6 devices with unified memory can run DeepSeek v2 at Q4_K_M (144.3 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.