Deepseek Coder 33B Instruct — Hardware Requirements & GPU Compatibility
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Deepseek Coder 33B Instruct is a 33.3B-parameter open language model from DeepSeek in the DeepSeek Coder family. It supports a context window of up to 16,384 tokens. At BF16 it needs about 67.51 GB of VRAM — see which GPUs and Macs can run it below.
7.6K downloads
573 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 33.3B
- Architecture
- LlamaForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 32,256
- Release Date
- 2024-03-07
- License
- Other
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HuggingFace
How Much VRAM Does Deepseek Coder 33B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 67.5 GB | 71.2 GB | 66.69 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Deepseek Coder 33B Instruct?
BF16 · 67.5 GBDeepseek Coder 33B Instruct (BF16) requires 67.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 88+ GB is recommended. Using the full 16K context window can add up to 3.6 GB, bringing total usage to 71.2 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Deepseek Coder 33B Instruct?
BF16 · 67.5 GB5 devices with unified memory can run Deepseek Coder 33B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
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Derivatives (1)
Frequently Asked Questions
- How much VRAM does Deepseek Coder 33B Instruct need?
Deepseek Coder 33B Instruct requires 67.5 GB of VRAM at BF16. Full 16K context adds up to 3.6 GB (71.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 33.3B × 16 bits ÷ 8 = 66.7 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.5 GB (at full 16K context)
VRAM usage by quantization
BF1667.5 GBBF16 + full context71.2 GB- Can NVIDIA GeForce RTX 5090 run Deepseek Coder 33B Instruct?
No — Deepseek Coder 33B Instruct requires at least 67.5 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Deepseek Coder 33B Instruct on a Mac?
Deepseek Coder 33B Instruct requires at least 67.5 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 33B Instruct locally?
Yes — Deepseek Coder 33B Instruct can run locally on consumer hardware. At BF16 quantization it needs 67.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Deepseek Coder 33B Instruct?
At BF16, Deepseek Coder 33B Instruct can reach ~43 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 ÷ 67.5 × 0.55 = ~43 tok/s
Estimated speed at BF16 (67.5 GB)
~43 tok/s~32 tok/s~27 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 33B Instruct?
At BF16, the download is about 66.69 GB.
- Which GPUs can run Deepseek Coder 33B Instruct?
No single consumer GPU has enough VRAM to run Deepseek Coder 33B Instruct at BF16 (67.5 GB). Multi-GPU or professional hardware is required.
- Which devices can run Deepseek Coder 33B Instruct?
5 devices with unified memory can run Deepseek Coder 33B Instruct at BF16 (67.5 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.