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DiffusionGemma 26B A4B
最先端
1.7Mダウンロード1.1KいいねJun 2026公開日262K トークンコンテキストApache 2.0ライセンス68 良好品質
DiffusionGemma 26B A4B (25.799999237060547B parameters) requires approximately 22.4 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 4B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 26 GB of VRAM.
はじめに
— コピー&ペーストでローカル実行Copy-paste commands to run DiffusionGemma 26B A4B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "google/diffusiongemma-26B-A4B-it" \
--hf-file "diffusiongemma-26B-A4B-it-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
Parameters25.8B (4B active)
Architecturemoe (MoE)
Context262K tokens
Modalitytext
Min RAM10.1 GB
Rec. RAM15.7 GB (Q4_K_M)
LicenseApache 2.0
FamilyGemma
✓ Vision✓ Chat✓ Reasoning
About this model
- •Block-diffusion decoding — generates blocks of tokens in parallel rather than strictly left-to-right.
- •MoE efficiency: 128 experts, 8 active per token (~4B activated of 26B total).
- •Gemma-based multimodal (image-text) backbone with 256K context.
- •Research-oriented alternative to autoregressive LLMs.
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このモデルを実行
量子化オプション
量子化レベル別VRAM推定値
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.1 GB | Low | — |
Q3_K_S | 3 | 12.6 GB | Low | — |
NVFP4 | 4 | 14.4 GB | Medium | — |
Q4_K_M | 4 | 15.7 GB | Medium | — |
Q5_K_M | 5 | 18.6 GB | High | — |
Q6_K | 6 | 21.2 GB | High | — |
Q8_0 | 8 | 27.6 GB | Very High | — |
F16 | 16 | 52.9 GB | Maximum | — |
ハードウェア互換性
全ハードウェアの適合度推定
Computing compatibility...
メモリ内訳
Reference: RTX 2060 6GB
Weights15.7 GB
KV Cache3.7 GB
Runtime2.4 GB
Headroom0.6 GB
よくある質問
FAQ — DiffusionGemma 26B A4B
関連項目