Deepseek Moe 16B Base — Hardware Requirements & GPU Compatibility
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Deepseek Moe 16B Base is a 16.4B-parameter open language model from DeepSeek in the DeepSeek family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 10.60 GB of VRAM — see which GPUs and Macs can run it below.
36.1K downloads
149 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek
- Parameters
- 16.4B
- Architecture
- DeepseekForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2024-01-08
- License
- Other
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HuggingFace
How Much VRAM Does Deepseek Moe 16B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 7.7 GB | 8.2 GB | 6.96 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 8.8 GB | 9.2 GB | 7.98 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 10.6 GB | 11.1 GB | 9.83 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 12.4 GB | 12.9 GB | 11.67 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 14.3 GB | 14.8 GB | 13.51 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 17.1 GB | 17.6 GB | 16.38 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 33.5 GB | 34.0 GB | 32.75 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Deepseek Moe 16B Base?
Q4_K_M · 10.6 GBDeepseek Moe 16B Base (Q4_K_M) requires 10.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 14+ GB is recommended. Using the full 4K context window can add up to 0.5 GB, bringing total usage to 11.1 GB. 27 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Deepseek Moe 16B Base?
Q4_K_M · 10.6 GB27 devices with unified memory can run Deepseek Moe 16B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Deepseek Moe 16B Base need?
Deepseek Moe 16B Base requires 10.6 GB of VRAM at Q4_K_M, or 33.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 16.4B × 4.8 bits ÷ 8 = 9.8 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.3 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M10.6 GBQ4_K_M + full context11.1 GB- Can NVIDIA GeForce RTX 4090 run Deepseek Moe 16B Base?
Yes, at Q8_0 (17.1 GB) or lower. Higher quantizations like BF16 (33.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Deepseek Moe 16B Base?
For Deepseek Moe 16B Base, Q4_K_M (10.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (12.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.7 GB.
VRAM requirement by quantization
Q2_K7.7 GBQ4_K_M ★10.6 GBQ5_K_M12.4 GBQ6_K14.3 GBQ8_017.1 GBBF1633.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Deepseek Moe 16B Base on a Mac?
Deepseek Moe 16B Base requires at least 7.7 GB at Q2_K, 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 Moe 16B Base locally?
Yes — Deepseek Moe 16B Base can run locally on consumer hardware. At Q4_K_M quantization it needs 10.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Deepseek Moe 16B Base?
At Q4_K_M, Deepseek Moe 16B Base can reach ~275 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~62 tok/s. 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 ÷ 10.6 × 0.55 = ~275 tok/s
Estimated speed at Q4_K_M (10.6 GB)
~275 tok/s~62 tok/s~206 tok/s~170 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Deepseek Moe 16B Base?
At Q4_K_M, the download is about 9.83 GB. The full-precision BF16 version is 32.75 GB. The smallest option (Q2_K) is 6.96 GB.
- Which GPUs can run Deepseek Moe 16B Base?
27 consumer GPUs can run Deepseek Moe 16B Base at Q4_K_M (10.6 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Deepseek Moe 16B Base?
27 devices with unified memory can run Deepseek Moe 16B Base at Q4_K_M (10.6 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.