Ling-3.0-flash-VL GGUF

GGUF conversions of inclusionAI/Ling-3.0-flash-VL

Built upon Ling-3.0-flash, it brings visual information into the complete process of understanding, reasoning, acting, and verification—advancing beyond image and video perception to solving real-world tasks through vision. With 124B total parameters, only 5.5B activated parameters per token, support for image and video inputs, and a context window of up to 256K tokens, Ling-3.0-flash-VL delivers powerful multimodal reasoning and agentic capabilities with exceptional efficiency.

Every text quant requires the bundled 875 MB mmproj-model-f16.gguf file for vision input.
Text-only chat works without it.

🦙🦙 llama.cpp 🦙🦙

🎉 Now supported in stock llama.cpp 🎉
Merged 2026-09-24 in #29151 (commit f830688e9).
Any build b11190 or newer loads these files as-is.

📝 Note: the GGUFs in this repo were re-published on 2026-09-22 under the final bailingmoe3 architecture name. Files downloaded before that date, or builds of the obsolete temporary ling3-vl branch are incompatible. You will need to re-download to use llama.cpp on build b11190 or newer.

To run with llama-server:

llama-server \
  -m Ling-3.0-flash-VL-Q4_K_M.gguf \
  --mmproj mmproj-model-f16.gguf \
  --jinja

Quant Sizing

Generally...
Larger files = More precision.
Smaller files = More compression = More slop and misbehavin'.

Weights and context share your memory, so be sure to leave headroom.

With less VRAM than the file size, keep the experts on CPU and the rest on GPU, e.g.:

llama-server \
  -m Ling-3.0-flash-VL-Q4_K_M.gguf \
  --mmproj mmproj-model-f16.gguf \
  -ngl 99 -ot "ffn_.*_exps\.weight=CPU" -c 32768 \
  --jinja

Usage

Recommended sampling from the source model card: temperature 0.6, top_p 0.95, top_k 20. Thinking mode is on by default; disable per request with "chat_template_kwargs": {"enable_thinking": false}.

Images

./build/bin/llama-server \
  -m Ling-3.0-flash-VL-Q4_K_M.gguf \
  --mmproj mmproj-model-f16.gguf \
  -c 131072 \
  -ngl auto \
  --flash-attn auto \
  --temp 0.6 --top-p 0.95 --top-k 20 \
  --jinja

Then attach an image in the web UI, or via the API:

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Describe this image."},
        {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
      ]
    }]
  }'

Video

Video input uses the same chat API with video_url content parts. Frames are sampled and encoded by the same vision tower.

Unlike the text-only Ling-3.0-flash GGUFs, these files contain no MTP/NextN block: the VL release does not ship one. Speculative drafting via --spec-type draft-mtp is not available for VL.

Long context (256K)

The GGUFs declare a native 131,072-token context. The advertised 256K window is reached with YaRN at factor 2, mirroring the upstream Ling-3.0-flash-VL recipe (yarn, factor 2.0, original context 131072):

llama-server \
  -m Ling-3.0-flash-VL-Q4_K_M.gguf \
  --mmproj mmproj-model-f16.gguf \
  -c 262144 \
  --rope-scaling yarn --rope-scale 2 --yarn-orig-ctx 131072 \
  --jinja

The attention KV cache scales with -c, doubling KV memory versus the native 131,072. Long-context quality at 256K was not validated here, the flags simply mirror the upstream recommendation.

Speculative decoding (DSpark)

The Ling-3.0-flash DSpark draft heads are compatible with the VL model: same vocabulary, and acceptance on VL is at least as good as on the text-only model the draft was trained for.

Measured with llama-server (VL Q6_K target, --spec-type draft-dspark --spec-draft-n-max 8, 32K context, 24 requests):

Draft acceptance on VL Q6_K: 0.32 (Q4_K_M draft), 0.30 (Q2_K draft). The same Q4_K_M draft measures 0.26 against text-only Ling-3.0-flash.

llama-server \
  -m Ling-3.0-flash-VL-Q6_K.gguf \
  -md Ling-3.0-flash-DSpark-Q4_K_M.gguf \
  --spec-type draft-dspark --spec-draft-n-max 8 \
  -ngl 99 -ngld 99 \
  --mmproj mmproj-model-f16.gguf \
  --jinja

The DSpark draft's attention does not use flash attention, so its compute buffer grows linearly with context length. It also carries its own KV cache; keep it at f16, quantizing it with -ctkd q4_0 -ctvd q4_0 was measured to cut decode speed by a further ~40%.

Benchmark your own stack before adopting the draft: the 1.63x above is from a bare benchmark harness (single role, 32K context). In a full serving stack (7 mixed GPUs, vision encoder loaded, q4_0 target KV cache, 48K context) the same draft measured 24.1 tok/s versus 38.1 tok/s with no draft at all.

Additional MoE Information

MoE placement can be adjusted for available VRAM with -ncmoe N.

Native context is 128K; 256K is available via YaRN (see Long context above).

Conversion and Quantization

Taken directly from the released inclusionAI/Ling-3.0-flash-VL BF16 safetensors.

Conversion-specific tensor transformations match the text-only Ling-3.0-flash conversions:

• A_log stored as exp(A_log)
• MLA kv_b_proj split into separate K and V tensors, with the K tensor transposed
• KDA convolution weights reshaped for llama.cpp
• Per-expert tensors stacked into GGUF expert tensors
• KDA and MLA g_proj tensors mapped separately

Vision tower and projector tensors live in the separate mmproj GGUF: Conv3D patch embedding, learned position embeddings, 27 attention blocks, a norm-only merger, and the two-layer projector.

Norms, routing tensors, expert routing bias, KDA state scalars, dt_bias, and convolution weights remain F32.

Notes

The text GGUF contains 42 blocks:

• 35 KDA layers
• 7 gated MLA layers at zero-based indices 5, 11, 17, 23, 29, 35, and 41

(No MTP/NextN block, unlike the text-only flash GGUFs.)

The first two layers use dense FFNs. The remaining layers use 512 routed experts with top-8 selection plus one shared expert. Routing uses sigmoid scoring, expert bias, eight expert groups, and four selected groups.

Position encoding is M-RoPE with sections [8, 12, 12], shared between text and vision positions.

Validation Completed

• BF16 architecture load and tensor round-trip (test-llama-archs, MoE fixture)
• mmproj GGUF round-trip: 334 tensors, ling3vl_merger projector
• End-to-end image and video inference on llama-server (Q4_K_M + mmproj)

Build

# Ling 3.0 VL support is in stock master (b11190+):
git clone https://github.com/ggml-org/llama.cpp.git

cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j --target llama-cli llama-server

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