DeepSeek Coder v2 Instruct — Hardware Requirements & GPU Compatibility
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DeepSeek Coder v2 Instruct is a 235.7B-parameter open language model from DeepSeek in the DeepSeek Coder family. It supports a context window of up to 163,840 tokens. At BF16 it needs about 474.30 GB of VRAM — see which GPUs and Macs can run it below.
4.3K downloads
688 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 235.7B
- Architecture
- DeepseekV2ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 102,400
- License
- Other
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HuggingFace
How Much VRAM Does DeepSeek Coder v2 Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 474.3 GB | 673.1 GB | 471.48 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run DeepSeek Coder v2 Instruct?
BF16 · 474.3 GBDeepSeek Coder v2 Instruct (BF16) requires 474.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 617+ GB is recommended. Using the full 164K context window can add up to 198.8 GB, bringing total usage to 673.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run DeepSeek Coder v2 Instruct?
BF16 · 474.3 GB2 devices with unified memory can run DeepSeek Coder v2 Instruct, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightBenchmarks
View all 1 →Related Models
Frequently Asked Questions
- How much VRAM does DeepSeek Coder v2 Instruct need?
DeepSeek Coder v2 Instruct requires 474.3 GB of VRAM at BF16. Full 164K context adds up to 198.8 GB (673.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 235.7B × 16 bits ÷ 8 = 471.5 GB
KV Cache + Overhead ≈ 2.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 201.6 GB (at full 164K context)
VRAM usage by quantization
BF16474.3 GBBF16 + full context673.1 GB- Can NVIDIA GeForce RTX 5090 run DeepSeek Coder v2 Instruct?
No — DeepSeek Coder v2 Instruct requires at least 474.3 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run DeepSeek Coder v2 Instruct on a Mac?
DeepSeek Coder v2 Instruct requires at least 474.3 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 Instruct locally?
Yes — DeepSeek Coder v2 Instruct can run locally on consumer hardware. At BF16 quantization it needs 474.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of DeepSeek Coder v2 Instruct?
At BF16, the download is about 471.48 GB.
- Which GPUs can run DeepSeek Coder v2 Instruct?
No single consumer GPU has enough VRAM to run DeepSeek Coder v2 Instruct at BF16 (474.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run DeepSeek Coder v2 Instruct?
2 devices with unified memory can run DeepSeek Coder v2 Instruct at BF16 (474.3 GB), including 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.