DeepSeek v2 Lite — Hardware Requirements & GPU Compatibility
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DeepSeek V2 Lite is a compact mixture-of-experts model with 15.7 billion total parameters, designed to deliver a strong quality-to-compute ratio for general chat and instruction following. It uses the same innovative MLA (Multi-Head Latent Attention) architecture as the larger V2, which reduces memory requirements during inference. With its modest parameter count, V2 Lite runs comfortably on a single consumer GPU, making it accessible to users who want to try DeepSeek's MoE approach without needing specialized hardware. It handles everyday conversational tasks, summarization, and light analysis well, offering a practical entry point into the DeepSeek model family.
226.3K downloads
168 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek V2
- Parameters
- 15.7B
- Architecture
- DeepseekV2ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2024-06-25
- License
- Other
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HuggingFace
How Much VRAM Does DeepSeek v2 Lite Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 32.2 GB | 68.0 GB | 31.41 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run DeepSeek v2 Lite?
BF16 · 32.2 GBDeepSeek v2 Lite (BF16) requires 32.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 42+ GB is recommended. Using the full 164K context window can add up to 35.8 GB, bringing total usage to 68.0 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run DeepSeek v2 Lite?
BF16 · 32.2 GB13 devices with unified memory can run DeepSeek v2 Lite, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does DeepSeek v2 Lite need?
DeepSeek v2 Lite requires 32.2 GB of VRAM at BF16. Full 164K context adds up to 35.8 GB (68.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 15.7B × 16 bits ÷ 8 = 31.4 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 36.6 GB (at full 164K context)
VRAM usage by quantization
BF1632.2 GBBF16 + full context68.0 GB- Can NVIDIA GeForce RTX 5090 run DeepSeek v2 Lite?
No — DeepSeek v2 Lite requires at least 32.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run DeepSeek v2 Lite on a Mac?
DeepSeek v2 Lite requires at least 32.2 GB at BF16, 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 Lite locally?
Yes — DeepSeek v2 Lite can run locally on consumer hardware. At BF16 quantization it needs 32.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek v2 Lite?
At BF16, DeepSeek v2 Lite can reach ~91 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 32.2 × 0.55 = ~91 tok/s
Estimated speed at BF16 (32.2 GB)
AMD Instinct MI300X~91 tok/sNVIDIA H100 SXM~68 tok/sAMD Instinct MI250X~56 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 Lite?
At BF16, the download is about 31.41 GB.