DeepSeek V2.5 — Hardware Requirements & GPU Compatibility
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DeepSeek V2.5 is a 235.7B-parameter open language model from DeepSeek in the DeepSeek V2 family. It supports a context window of up to 163,840 tokens. At Q4_K_M it needs about 144.26 GB of VRAM — see which GPUs and Macs can run it below.
8.5K downloads
734 likesSpecifications
- Publisher
- DeepSeek
- Family
- DeepSeek V2
- Parameters
- 235.7B
- Architecture
- DeepseekV2ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 102,400
- License
- Other
Get Started
HuggingFace
How Much VRAM Does DeepSeek V2.5 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 144.3 GB | 343.1 GB | 141.44 GB | 4-bit medium quantization — most popular sweet spot |
| Q6_K | 6.60 | 197.3 GB | 396.1 GB | 194.49 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 238.6 GB | 437.4 GB | 235.74 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run DeepSeek V2.5?
Q4_K_M · 144.3 GBDeepSeek V2.5 (Q4_K_M) requires 144.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 188+ GB is recommended. Using the full 164K context window can add up to 198.8 GB, bringing total usage to 343.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run DeepSeek V2.5?
Q4_K_M · 144.3 GB4 devices with unified memory can run DeepSeek V2.5, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Pro M2 Ultra (192 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightBenchmarks
View all 1 →Related Models
Frequently Asked Questions
- How much VRAM does DeepSeek V2.5 need?
DeepSeek V2.5 requires 144.3 GB of VRAM at Q4_K_M, or 238.6 GB at Q8_0. Full 164K context adds up to 198.8 GB (343.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 235.7B × 4.8 bits ÷ 8 = 141.4 GB
KV Cache + Overhead ≈ 2.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 201.7 GB (at full 164K context)
VRAM usage by quantization
Q4_K_M144.3 GBQ4_K_M + full context343.1 GB- Can NVIDIA GeForce RTX 5090 run DeepSeek V2.5?
No — DeepSeek V2.5 requires at least 144.3 GB at Q4_K_M, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for DeepSeek V2.5?
For DeepSeek V2.5, Q4_K_M (144.3 GB) offers the best balance of quality and VRAM usage. Q6_K (197.3 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★144.3 GBQ6_K197.3 GBQ8_0238.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek V2.5 on a Mac?
DeepSeek V2.5 requires at least 144.3 GB at Q4_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 V2.5 locally?
Yes — DeepSeek V2.5 can run locally on consumer hardware. At Q4_K_M quantization it needs 144.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek V2.5?
At Q4_K_M, DeepSeek V2.5 can reach ~20 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 ÷ 144.3 × 0.55 = ~20 tok/s
Estimated speed at Q4_K_M (144.3 GB)
~20 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of DeepSeek V2.5?
At Q4_K_M, the download is about 141.44 GB. The full-precision Q8_0 version is 235.74 GB.
- Which GPUs can run DeepSeek V2.5?
No single consumer GPU has enough VRAM to run DeepSeek V2.5 at Q4_K_M (144.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run DeepSeek V2.5?
4 devices with unified memory can run DeepSeek V2.5 at Q4_K_M (144.3 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), 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.