deepseek-moe-16b-cpu-offload-gguf

DeepSeek MoE 16B Base - F16 GGUF with MoE CPU Offloading Support

F16 GGUF conversion of deepseek-ai/deepseek-moe-16b-base with Rust bindings for llama.cpp's MoE CPU offloading functionality.

Model Details

• Base Model: deepseek-ai/deepseek-moe-16b-base
• Format: GGUF F16 precision
• File Size: 31GB
• Parameters: 16.4B total (2.8B active per token)
• Architecture: 28 layers, 64 regular experts + 2 shared experts, 6 active per token
• Context Length: 4K tokens
• Converted by: MikeKuykendall

MoE CPU Offloading

This model supports MoE CPU offloading via llama.cpp (implemented in PR #15077). Shimmy provides Rust bindings for this functionality, enabling:

• VRAM Reduction: 92.5% (30.1GB → 2.3GB measured on GH200)
• Performance Trade-off: 4.1x slower generation (26.8 → 6.5 TPS)
• Use Case: Running 16B parameter MoE on consumer GPUs (<4GB VRAM)

Controlled Baseline (NVIDIA GH200, N=3)

Trade-off: Memory for speed. Best for VRAM-constrained scenarios where generation speed is less critical than model size.

Unique Architecture

DeepSeek MoE uses a dual-expert architecture (64 regular + 2 shared experts), validated to work correctly with CPU offloading:

• Regular experts: ffn_gate_exps.weight, ffn_down_exps.weight, ffn_up_exps.weight
• Shared experts: ffn_gate_shexp.weight, ffn_down_shexp.weight, ffn_up_shexp.weight

Download

huggingface-cli download MikeKuykendall/deepseek-moe-16b-cpu-offload-gguf \
  --include "deepseek-moe-16b-f16.gguf" \
  --local-dir ./models

Usage

llama.cpp (CPU Offloading)

# Standard loading (requires ~32GB VRAM)
./llama-server -m deepseek-moe-16b-f16.gguf -c 4096

# With MoE CPU offloading (requires ~3GB VRAM + 32GB RAM)
./llama-server -m deepseek-moe-16b-f16.gguf -c 4096 --cpu-moe

Shimmy (Rust Bindings)

# Install Shimmy
cargo install --git https://github.com/Michael-A-Kuykendall/shimmy --features llama-cuda

# Standard loading
shimmy serve --model deepseek-moe-16b-f16.gguf

# With MoE CPU offloading
shimmy serve --model deepseek-moe-16b-f16.gguf --cpu-moe

# Query the API
curl http://localhost:11435/api/generate \
  -d '{
    "model": "deepseek-moe-16b",
    "prompt": "Explain the architecture of DeepSeek MoE",
    "max_tokens": 256,
    "stream": false
  }'

Performance Notes

Standard GPU Loading:

• VRAM: 30.1GB
• Speed: 26.8 TPS
• Latency: 426ms TTFT
• Use when: VRAM is plentiful, speed is critical

CPU Offloading:

• VRAM: 2.3GB (92.5% reduction)
• Speed: 6.5 TPS (4.1x slower)
• Latency: 1,643ms TTFT
• Use when: Limited VRAM, speed less critical

Original Model

• Developers: DeepSeek AI
• License: Apache 2.0
• Paper: DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
• Languages: English, Chinese

Technical Validation

Full validation report with controlled baselines: Shimmy MoE CPU Offloading Technical Report

Citation

@article{dai2024deepseekmoe,
  title={DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models},
  author={Dai, Damai and others},
  journal={arXiv preprint arXiv:2401.06066},
  year={2024}
}

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GGUF conversion and MoE offloading validation by MikeKuykendall