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DeepSeek V3.1 (Q4_K_M) — 54.7 GBon Apple M2 Pro 16GB

DeepSeek
Code Multilingual Thinking Tool Calls
Q4_K_M Apple M2 Pro 16GB

Overview

DeepSeek V3.1 is a 684.53B parameter moe language model by DeepSeek, with code, multilingual, thinking, tool-calls capabilities. It supports a context window of up to 163,840 tokens.

DeepSeek V3.1 is a 685-billion-parameter Mixture-of-Experts model from DeepSeek, activating 8 of 256 experts per token plus one shared expert. It delivers frontier-level performance on code generation, reasoning, and multilingual tasks while using far fewer active parameters per inference step than comparably sized dense models. The model supports thinking mode, tool calling, and nine languages. With a 160K context window, it requires multi-GPU or distributed setups but quantizes down to Q2 levels for reduced VRAM footprint.

At Q4_K_M quantization (medium quality tier), the model weighs 377.56 GB. This exceeds the 16 GB of VRAM on Apple M2 Pro 16GB. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.

Hardware Requirements

Model size 377.56 GB
VRAM available 16 GB
VRAM used 54.7 GB
Min RAM required 377.6 GB
GPU layers 0 / 61
Context size 32,768
Backend metal
Flash attention No

Performance Notes

Deploy

Install llama.cpp

brew install llama.cpp

Download Model

curl -L -o deepseek-v3-1.gguf "https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF/resolve/main/Q4_K_M/DeepSeek-V3.1-Q4_K_M-00001-of-00009.gguf"

Start Server

llama-server \
  -m deepseek-v3-1.gguf \
  --n-gpu-layers 0 \
  --ctx-size 32768

Verify

curl http://localhost:8080/health

Frequently Asked Questions

How much VRAM does DeepSeek V3.1 (Q4_K_M) need?

The Q4_K_M quantization of DeepSeek V3.1 requires 377.56 GB. The 16 GB of VRAM on Apple M2 Pro 16GB is insufficient for GPU layers, so inference runs on CPU.

Can I run DeepSeek V3.1 on Apple M2 Pro 16GB?

It is possible but not recommended. Apple M2 Pro 16GB does not have enough VRAM to accelerate DeepSeek V3.1 (Q4_K_M), so inference will rely on CPU and system RAM.

What is quantization?

Quantization reduces a model's numerical precision from its original floating-point format to a more compact representation. This shrinks the file size and VRAM footprint, making it possible to run large models on consumer hardware. The trade-off is a small reduction in output quality. Q4_K_M compresses DeepSeek V3.1 from its original size down to 377.56 GB.

What quantization should I choose for DeepSeek V3.1?

Q4_K_M is a medium-quality quantization. Higher-quality quants (Q8, Q6) preserve more model accuracy but need more VRAM. Lower quants (Q4, Q3, Q2) reduce VRAM usage at the cost of some quality. Choose based on your available hardware and quality requirements.

Why are some layers offloaded to CPU?

Apple M2 Pro 16GB has 16 GB of VRAM, but DeepSeek V3.1 (Q4_K_M) requires approximately 377.56 GB. Only 0 of 61 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.

What is MoE and how does it affect deployment?

DeepSeek V3.1 uses a Mixture-of-Experts (MoE) architecture with 256 experts, of which 8 are active per token. This means only a fraction of the model weights are used for each inference step, allowing MoE models to be larger in total parameter count while remaining efficient at inference time.

Last updated: March 5, 2026