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thetom-ai/DeepSeek-V4-Flash-ConfigI-GGUF
DeepSeek-V4-Flash-ConfigI-GGUF is a text generation model from thetom-ai. Use it when you need the model to write or continue text. The card lists the license as mit.
284B-parameter MoE (21B active) in 95 GiB — fits and runs on 128 GB unified-memory boxes (DGX Spark, Mac). Config-I hybrid quantization at 2.88 bpw effective.
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.gguf102 GB · 100%
From the Hugging Face model README
284B-parameter MoE (21B active) in 95 GiB — fits and runs on 128 GB unified-memory boxes (DGX Spark, Mac). Config-I hybrid quantization at 2.88 bpw effective.
Config-I quantization of deepseek-ai/DeepSeek-V4-Flash-0731 — the official 2026-07-31 release, superseding the preview — (43 layers, 256 routed experts top-6, hash routing, MLA attention, mHC hyper-connections, DSA indexer). Sibling release: Config-I MLX for Apple Silicon.
📖 How to actually run it well: the offlabel operating guide covers the backend traps, sampling, the repetition-loop fix, and the DwarfStar speed comparison in one place.
This GGUF uses the TurboQuant TQ3_1S weight type, so it needs the TurboQuant llama.cpp fork rather than stock llama.cpp:
git clone https://github.com/TheTom/llama-cpp-turboquant
cd llama-cpp-turboquant # default branch is what you want
cmake -B build -DGGML_CUDA=ON # or -DGGML_METAL=ON on a Mac
cmake --build build -j
Verified against the fork's default branch (feature/turboquant-kv-cache): loads and generates correctly on both Metal and CUDA.
| Tensor group | Type | bpw |
|---|---|---|
| Expert gate/up (all layers) | Q2_0 | 2.25 |
| Expert down, attention, indexer, compressor | TQ3_1S (WHT-rotated) | 4.0 |
| Token embeddings | q8_0 | 8.5 |
| Output head | q6_K | 6.6 |
| Router gates, mHC mixing, APE, norms, sinks | f32/bf16 | — |
| Size | |
|---|---|
| FP8/FP4 source | 167 GB |
| BF16 equivalent | ~570 GB |
| Config-I hybrid (2.88 bpw) | 95 GiB (102 GB), 3 split files ≤45 GB |
Observed behaviors at 2.88 bpw (honesty notes, measured under heavy I/O contention — quiet-condition re-runs pending):
</think> content stream can degenerate into repetition while the correct answer sits intact in the reasoning block — parse reasoning_content when serving with reasoning enabled (observed under greedy decoding; official sampling mitigates).TQ_NO_ROTATE=1 ./build/bin/llama-cli -m DeepSeek-V4-Flash-0731-ConfigI-00001-of-00003.gguf \
-ngl 99 -c 8192 --temp 1.0 --top-p 0.95 -p "your prompt" -st < /dev/null
Inherits the DeepSeek model license from the base model. Quantization by thetom-ai.