DeepSeek v3 — Hardware Requirements & GPU Compatibility
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DeepSeek v3 is a 684.5B-parameter open language model from DeepSeek in the DeepSeek V3 family. It supports a context window of up to 163,840 tokens. At Q4_K_M it needs about 414.60 GB of VRAM — see which GPUs and Macs can run it below.
1.1M downloads
4.1K likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek V3
- Parameters
- 684.5B
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 129,280
Get Started
HuggingFace
How Much VRAM Does DeepSeek v3 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_M | 3.90 | 337.6 GB | 620.6 GB | 333.71 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 414.6 GB | 697.6 GB | 410.72 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 491.6 GB | 774.6 GB | 487.73 GB | 5-bit medium quantization — good quality/size tradeoff |
Which GPUs Can Run DeepSeek v3?
Q4_K_M · 414.6 GBDeepSeek v3 (Q4_K_M) requires 414.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 539+ GB is recommended. Using the full 164K context window can add up to 283.0 GB, bringing total usage to 697.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run DeepSeek v3?
Q4_K_M · 414.6 GB2 devices with unified memory can run DeepSeek v3, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
View all 9 →Related Models
Frequently Asked Questions
- How much VRAM does DeepSeek v3 need?
DeepSeek v3 requires 414.6 GB of VRAM at Q4_K_M, or 491.6 GB at Q5_K_M. Full 164K context adds up to 283.0 GB (697.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 684.5B × 4.8 bits ÷ 8 = 410.7 GB
KV Cache + Overhead ≈ 3.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 286.9 GB (at full 164K context)
VRAM usage by quantization
Q4_K_M414.6 GBQ4_K_M + full context697.6 GB- Can NVIDIA GeForce RTX 5090 run DeepSeek v3?
No — DeepSeek v3 requires at least 337.6 GB at Q3_K_M, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for DeepSeek v3?
For DeepSeek v3, Q4_K_M (414.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (491.6 GB) provides better quality if you have the VRAM. The smallest option is Q3_K_M at 337.6 GB.
VRAM requirement by quantization
Q3_K_M337.6 GBQ4_K_M ★414.6 GBQ5_K_M491.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek v3 on a Mac?
DeepSeek v3 requires at least 337.6 GB at Q3_K_M, 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 v3 locally?
Yes — DeepSeek v3 can run locally on consumer hardware. At Q4_K_M quantization it needs 414.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of DeepSeek v3?
At Q4_K_M, the download is about 410.72 GB. The full-precision Q5_K_M version is 487.73 GB. The smallest option (Q3_K_M) is 333.71 GB.
- Which GPUs can run DeepSeek v3?
No single consumer GPU has enough VRAM to run DeepSeek v3 at Q4_K_M (414.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run DeepSeek v3?
2 devices with unified memory can run DeepSeek v3 at Q4_K_M (414.6 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.