Instructions to use batiai/DeepSeek-V4-Pro-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use batiai/DeepSeek-V4-Pro-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="batiai/DeepSeek-V4-Pro-GGUF",
	filename="deepseek-ai-DeepSeek-V4-Pro-Q3_K_M-00001-of-00017.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use batiai/DeepSeek-V4-Pro-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M

Use Docker

docker model run hf.co/batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use batiai/DeepSeek-V4-Pro-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "batiai/DeepSeek-V4-Pro-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "batiai/DeepSeek-V4-Pro-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M

Ollama
How to use batiai/DeepSeek-V4-Pro-GGUF with Ollama:
```
ollama run hf.co/batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M
```

Unsloth Studio new

How to use batiai/DeepSeek-V4-Pro-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/DeepSeek-V4-Pro-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/DeepSeek-V4-Pro-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for batiai/DeepSeek-V4-Pro-GGUF to start chatting

Docker Model Runner
How to use batiai/DeepSeek-V4-Pro-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M
```

Lemonade

How to use batiai/DeepSeek-V4-Pro-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/DeepSeek-V4-Pro-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-V4-Pro-GGUF-Q4_K_M

List all available models

lemonade list

DeepSeek V4-Pro GGUF — BatiAI Frontier

⚠️ Early Access — requires bati.cpp to run. DeepSeek V4-Pro is the 1.6 trillion parameter flagship of the DeepSeek V4 series, released 2026-05-06. It is not yet supported by ggml-org/llama.cpp master. This GGUF was converted with batiai/bati.cpp v0.1.2 — BatiAI's own inference fork. Inference requires the same library. Ollama is not yet compatible (will auto-update once mainline merges V4 support).

Why DeepSeek V4-Pro?

The largest open-weights frontier model available in GGUF form. Top-tier on coding, reasoning, and agentic benchmarks per DeepSeek's official release.

1.6 trillion total parameters / 49B activated per token (largest open-weights LLM as of 2026-05)
DeepSeek-V4-Pro-Max (extended reasoning) closes the gap with frontier closed-source models on reasoning + agentic tasks
Hybrid Attention: Compressed Sparse Attention (CSA) + Heavily Compressed Attention (HCA) — 27% inference FLOPs and 10% KV cache vs DeepSeek-V3.2 at 1M context
Manifold-Constrained Hyper-Connections (mHC) — strengthens residual paths for signal propagation across 61 layers
Muon Optimizer training — faster convergence, greater stability
1M context window native
32T+ tokens pretrained + two-stage post-training (independent domain experts → on-policy distillation)
MIT license — fully open

Quick Start

⚠️ This is a workstation/cluster model. Even Mac Studio M3 Ultra 512GB cannot fit Q3. Plan for 768GB+ unified memory or multi-Ultra cluster / 8×A100 80GB / H100 node.

# Q3_K_M (smallest, 698GB — 768GB+ unified memory)
hf download batiai/DeepSeek-V4-Pro-GGUF --include "*Q3_K_M*"

# Q4_K_M (balanced, 900GB — 1TB+ recommended)
hf download batiai/DeepSeek-V4-Pro-GGUF --include "*Q4_K_M*"

# Q5_K_M (higher fidelity, 1.06TB — 2× M3 Ultra cluster)
hf download batiai/DeepSeek-V4-Pro-GGUF --include "*Q5_K_M*"

# Q8_0 (near-original FP4→Q8 dequant, 1.67TB — multi-node)
hf download batiai/DeepSeek-V4-Pro-GGUF --include "*Q8_0*"

Available Quants

Quant	Size	Shards	Min RAM	Target Hardware
Q3_K_M	698 GB	17 × ~43 GB	768 GB	M3 Ultra 512GB cluster, 8×A100 80GB
Q4_K_M	900 GB	21 × ~43 GB	1 TB	2× M3 Ultra 512GB, 16×A100
Q5_K_M	1.06 TB	26 × ~43 GB	1.2 TB	2× M3 Ultra, H100 node
Q8_0	1.67 TB	38 × ~45 GB	1.8 TB	4× M3 Ultra cluster, H100 node 8×

All quants signed by BatiAI (general.author=BatiAI, general.url=https://flow.bati.ai).

Note: IQ-quants (IQ3_XXS / IQ4_XS) are tracked in bati.cpp v0.2.0. They require imatrix calibration, and llama-imatrix currently segfaults during V4 model context init in the fork. Will be added once that path is fixed (or once mainline llama.cpp merges DeepSeek V4 support). K-quants above use bati.cpp v0.1.2's integer-tensor pass-through patch + --allow-requantize from a Q8_0 base.

Hardware Reality Check

Your System	Q3 (698GB)	Q4 (900GB)	Q5 (1.06TB)	Q8 (1.67TB)
Mac 128GB	❌	❌	❌	❌
Mac 192GB	❌	❌	❌	❌
Mac 256GB	❌	❌	❌	❌
Mac M3 Ultra 512GB	⚠️ heavy swap (impractical)	❌	❌	❌
2× M3 Ultra (1TB cluster)	✅	✅ tight	❌	❌
4× M3 Ultra cluster	✅	✅	✅	⚠️
8× A100 80GB (640GB total)	⚠️ tight	❌	❌	❌
8× H100 80GB (640GB total)	⚠️ tight	❌	❌	❌
8× H200 141GB (1.1TB total)	✅ Fast	✅ Fast	✅ tight	❌
DGX H200 / H100 node 1TB+	✅ Fast	✅ Fast	✅	⚠️
Multi-node H100/H200 cluster	✅	✅	✅	✅

Bottom line: V4-Pro is not a consumer Mac model. For Mac users with ≤256GB, use batiai/DeepSeek-V4-Flash-GGUF (284B-A13B, 127-282 GB) for the same architecture family in actually-runnable sizes.

How to run inference (build `bati.cpp`)

# 1. Clone + build BatiAI's inference library
git clone https://github.com/batiai/bati.cpp.git
cd bati.cpp
cmake -B build -DGGML_CUDA=ON               # Linux (recommended for V4-Pro scale)
# or: cmake -B build -DGGML_METAL=ON         # macOS (multi-Ultra cluster only)
cmake --build build -j 16 --target llama-cli llama-gguf-split llama-server

# 2. Download a quant + merge shards (Q3 example, 698GB → single GGUF)
hf download batiai/DeepSeek-V4-Pro-GGUF \
    --include "*Q3_K_M*" --local-dir ./v4-pro
build/bin/llama-gguf-split --merge \
    ./v4-pro/deepseek-ai-DeepSeek-V4-Pro-Q3_K_M-00001-of-00017.gguf \
    ./v4-pro/merged-Q3_K_M.gguf

# 3. Inference (CLI, single-node minimum spec)
build/bin/llama-cli \
    -m ./v4-pro/merged-Q3_K_M.gguf \
    -cnv -ngl 99 -c 8192 \
    --reasoning on --reasoning-budget 8192

# 4. Or run as a server (recommended for production)
build/bin/llama-server \
    -m ./v4-pro/merged-Q3_K_M.gguf \
    -ngl 99 -c 32768 --port 8080

Reasoning mode (DeepSeek-V4-Pro-Max)

DeepSeek-V4-Pro's "Max" mode uses extended reasoning budget for hardest tasks. Enable via:

build/bin/llama-cli -m merged.gguf --reasoning on --reasoning-budget 32768 -c 65536

DeepSeek-V4-Flash-Max approaches Pro-level reasoning with a larger budget, but for pure knowledge tasks and the most complex agentic workflows, Pro retains a clear lead per the official release notes.

Model details

Source: deepseek-ai/DeepSeek-V4-Pro
Architecture: 1.6T total / 49B active MoE — 61 layers, 7168 hidden, 384 routed experts (top-6), 1 KV head
Attention: CSA + HCA hybrid (1M native context)
Optimizer: Muon (training)
Innovation: Manifold-Constrained Hyper-Connections (mHC) for residual signal propagation
Original precision: FP4 + FP8 mixed (FP4 expert weights, FP8 attention) — quantization_config MXFP4 spec
This GGUF: Q8_0 dequantization base → K-quants via --allow-requantize
License: MIT

Quantization story (1.6T = engineering exercise)

V4-Pro is the largest open-weights model in BatiAI's catalog and required significant infrastructure tuning. Documented for future 1T+ MoE work:

Source size: 805 GB safetensors (FP4 mixed precision)
Q8_0 dequantized: 1.67 TB (2.07× FP4→Q8 expansion factor)
K-quants from Q8 base via --allow-requantize (avoids BF16 intermediate)
Memory peak: ~1.3 TB RAM+swap during convert (single 503GB-RAM machine + 1.2TB swap)
Disk allocation: NFS overflow to NAS — convert outfile directly to network storage to escape the 4TB local SSD limit (Q8 base + safetensors > 2.5 TB combined active size)
Convert tool: bati.cpp v0.1.2 convert_hf_to_gguf.py (FP4 → Q8 direct path, no BF16)
K-quants pipeline: llama-quantize --allow-requantize from Q8 first shard

This GGUF is the second public V4-Pro quantization after teamblobfish/DeepSeek-V4-Pro-GGUF, and the first with BatiAI metadata signing + full Q3/Q4/Q5/Q8 K-quant matrix.

What happens after mainline merges V4

When ggml-org/llama.cpp master merges DeepSeek V4 support:

Rebuild with mainline + run imatrix calibration (wikitext-2, 200 chunks — projected ~24 hours for 1.6T MoE)
Add IQ3_XXS, IQ4_XS quants (better quality at same/smaller size than Q3/Q4 K-quants)
Run real-hardware benchmarks (M3 Ultra cluster + H200 cluster)
bati.cpp's V4 support transitions to read-only archive (users migrate to mainline)

Watch this repo or the upstream DeepSeek V4 llama.cpp tracking issue for the update.

BatiAI signing

All GGUFs in this repo carry:

general.author = BatiAI
general.url = https://flow.bati.ai

About `bati.cpp`

batiai/bati.cpp is BatiAI's own inference library — a llama.cpp-based fork focused on Apple Silicon, frontier-model early access, and BatiAI's quantization standard. Built on top of ggml-org/llama.cpp and antirez/llama.cpp-deepseek-v4-flash (all MIT). See bati.cpp's ATTRIBUTION.md for full credits.

License

Inherits the source model license: MIT.

About BatiFlow

BatiFlow — free on-device AI automation for Mac.

Benchmarks will be added once Mac M3 Ultra cluster / H200 node measurements complete.

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GGUF

Model size

1.6T params

Architecture

deepseek4

Hardware compatibility

3-bit

4-bit

5-bit

8-bit

Model tree for batiai/DeepSeek-V4-Pro-GGUF

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deepseek-ai/DeepSeek-V4-Pro

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Collection including batiai/DeepSeek-V4-Pro-GGUF

🚀 Frontier MoE — 128B–1T

Collection

Largest open-weight LLMs, BatiAI-quantized. Mac-runnable from M4 Max 128GB to Mac Studio M3 Ultra 512GB. • 7 items • Updated 4 days ago