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.

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 Q8_0 version is 684.53 GB. The smallest option (IQ2_XXS) is 188.25 GB.

DeepSeek·DeepSeek V3·DeepseekV3ForCausalLM

DeepSeek v3 0324 — Hardware Requirements & GPU Compatibility

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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.

328.9K downloads 3.1K likes164K context

Specifications

Publisher: DeepSeek
Family: DeepSeek V3
Parameters: 684.5B
Architecture: DeepseekV3ForCausalLM
Context Length: 163,840 tokens
Vocabulary Size: 129,280
License: MIT

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HuggingFace

deepseek-ai/DeepSeek-V3-0324

How Much VRAM Does DeepSeek v3 0324 Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
IQ2_XXS	2.20	192.1 GB	475.1 GB	188.25 GB	Importance-weighted 2-bit, extreme compression — significant quality loss
IQ2_S	2.50	217.8 GB	500.8 GB	213.92 GB	Importance-weighted 2-bit, small
IQ2_M	2.70	234.9 GB	517.9 GB	231.03 GB	Importance-weighted 2-bit, medium
IQ3_XXS	3.10	269.1 GB	552.1 GB	265.26 GB	Importance-weighted 3-bit
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
Q3_K_L	4.10	354.7 GB	637.7 GB	350.82 GB	3-bit large quantization
IQ4_XS	4.30	371.8 GB	654.8 GB	367.94 GB	Importance-weighted 4-bit, compact
Q4_1	4.50	388.9 GB	671.9 GB	385.05 GB	4-bit legacy quantization with offset
Q4_K_S	4.50	388.9 GB	671.9 GB	385.05 GB	4-bit small quantization
IQ4_NL	4.50	388.9 GB	671.9 GB	385.05 GB	Importance-weighted 4-bit, non-linear
Q4_K_M	4.80	414.6 GB	697.6 GB	410.72 GB	4-bit medium quantization — most popular sweet spot
Q5_K_S	5.50	474.5 GB	757.5 GB	470.62 GB	5-bit small quantization
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 GB

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DeepSeek 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 GB

2 devices with unified memory can run DeepSeek v3 0324, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Runs great

— Plenty of headroom

NVIDIA DGX H100~42 tok/s NVIDIA DGX A100 640GB~26 tok/s

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 688.4 GB at Q8_0. 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_M
414.6 GB
Q4_K_M + full context
697.6 GB
Learn more about VRAM estimation →
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_XXS
192.1 GB~53%
Q2_K
294.8 GB~75%
IQ4_XS
371.8 GB~87%
Q4_K_M ★
414.6 GB~89%
Q5_K_S
474.5 GB~92%
Q8_0
688.4 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
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 Q8_0 version is 684.53 GB. The smallest option (IQ2_XXS) is 188.25 GB.