DeepSeek Coder v2 Lite Base — Hardware Requirements & GPU Compatibility
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4.8K downloads
105 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 15.7B
- Architecture
- DeepseekV2ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2024-07-03
- License
- Other
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HuggingFace
How Much VRAM Does DeepSeek Coder v2 Lite Base 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 Coder v2 Lite Base?
BF16 · 32.2 GBDeepSeek Coder v2 Lite Base (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 Coder v2 Lite Base?
BF16 · 32.2 GB13 devices with unified memory can run DeepSeek Coder v2 Lite Base, 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 Coder v2 Lite Base need?
DeepSeek Coder v2 Lite Base 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 Coder v2 Lite Base?
No — DeepSeek Coder v2 Lite Base requires at least 32.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run DeepSeek Coder v2 Lite Base on a Mac?
DeepSeek Coder v2 Lite Base 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 Coder v2 Lite Base locally?
Yes — DeepSeek Coder v2 Lite Base 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 Coder v2 Lite Base?
At BF16, DeepSeek Coder v2 Lite Base 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 Coder v2 Lite Base?
At BF16, the download is about 31.41 GB.
- Which GPUs can run DeepSeek Coder v2 Lite Base?
No single consumer GPU has enough VRAM to run DeepSeek Coder v2 Lite Base at BF16 (32.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run DeepSeek Coder v2 Lite Base?
13 devices with unified memory can run DeepSeek Coder v2 Lite Base at BF16 (32.2 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.