DeepSeek v3 0324 — Hardware Requirements & GPU Compatibility
Chat
DeepSeek V3 0324 is DeepSeek's flagship general-purpose chat model, featuring a 684.5 billion parameter mixture-of-experts architecture with roughly 37 billion parameters active per token. It delivers strong performance across a wide range of tasks including conversation, writing, analysis, coding, and instruction following, competing with the best closed-source models available. Like other large MoE models, V3 requires substantial memory to load all expert weights even though only a fraction are used during inference. Quantized versions make it feasible on multi-GPU setups, and its combination of broad capability with open weights has made it one of the most widely deployed open models for local and self-hosted use.
896.3K downloads
3.1K likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek V3
- Parameters
- 684.5B
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2025-03-24
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does DeepSeek v3 0324 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 294.8 GB | 577.8 GB | 290.93 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 303.4 GB | 586.3 GB | 299.48 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 337.6 GB | 620.6 GB | 333.71 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 346.1 GB | 629.1 GB | 342.27 GB | 4-bit legacy 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 |
| Q6_K | 6.60 | 568.6 GB | 851.6 GB | 564.74 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 688.4 GB | 971.4 GB | 684.53 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run DeepSeek v3 0324?
Q4_K_M · 414.6 GBDeepSeek v3 0324 (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 0324?
Q4_K_M · 414.6 GB2 devices with unified memory can run DeepSeek v3 0324, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download DeepSeek v3 0324
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does DeepSeek v3 0324 need?
DeepSeek v3 0324 requires 414.6 GB of VRAM at Q4_K_M, or 1372.9 GB at BF16. 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 0324?
No — DeepSeek v3 0324 requires at least 192.1 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for DeepSeek v3 0324?
For DeepSeek v3 0324, Q4_K_M (414.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (474.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 192.1 GB.
VRAM requirement by quantization
IQ2_XXS192.1 GBQ3_K_S303.4 GBIQ4_XS371.8 GBQ4_K_M ★414.6 GBQ5_K_S474.5 GBBF161372.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek v3 0324 on a Mac?
DeepSeek v3 0324 requires at least 192.1 GB at IQ2_XXS, 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 0324 locally?
Yes — DeepSeek v3 0324 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 0324?
At Q4_K_M, the download is about 410.72 GB. The full-precision BF16 version is 1369.06 GB. The smallest option (IQ2_XXS) is 188.25 GB.
- Which GPUs can run DeepSeek v3 0324?
No single consumer GPU has enough VRAM to run DeepSeek v3 0324 at Q4_K_M (414.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run DeepSeek v3 0324?
3 devices with unified memory can run DeepSeek v3 0324 at Q4_K_M (414.6 GB), including Mac Studio (M3 Ultra, 512GB), 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.