#9 Gemma 4 26B-A4B
Fast, Apache-2.0, strong on 24 GB; r/LocalLLaMA favorite for dialogue.
- Version
- gemma-4-26b-a4b-it
- Maker
- Google DeepMind
- Release date
- no published figure yet unverified (Hugging Face repo creation date unless noted)
- Architecture
- MoE (8 of 128 experts + 1 shared), hybrid local/global attention, multimodal
- Total params
- 25.2B
- Active params
- 3.8B
- Context length
- 256K
- License
- Apache-2.0
- Commercial notes
- Fully commercial
- Variants
- E2B
- E4B
- 12B
- 26B-A4B
- 31B dense
- Memory
- BF16/native: no published figure yet
FP8: no published figure yet
Q8: no published figure yet
Q4: no published figure yet
LLMCheck min RAM 18 GB - Hardware
- Single Spark (128GB): Yes
Dual Spark (256GB): Yes
Quad Spark (~512GB): Yes
24GB GPU: Yes
128GB Mac / Strix: Yes - Tokens/sec
- mac_mini_m6_32gb: 48 tok/s community, LM Studio (LLMCheck)
- Document Q&A strengths
- 256K context
- Image input
- Pairs with EmbeddingGemma 2 (same family)
- Embedding / reranker pairing
- {'embedding': 'google/embeddinggemma-2 (740M, 768-d, 100+ languages, 8K ctx)', 'reranker': None, 'source': 'https://huggingface.co/google/embeddinggemma-2', 'verified': 'same family; not a Google-prescribed pairing'}
- Runtimes
- Ollama: listed (gemma4)
llama.cpp: Yes (card mentions llama.cpp)
vLLM: no published figure yet unverified
LM Studio: Yes (used in M6 benchmark) - Hugging Face
- https://huggingface.co/google/gemma-4-26b-a4b-it
DGX Spark configs it fits
- Single Spark (128GB): Yes
- Dual Spark (256GB): Yes
- Quad Spark (~512GB): Yes
Spark fit is estimated from published memory sizes, not tested on Spark hardware.
Sources
- https://huggingface.co/google/gemma-4-26b-a4b-it
- https://llmcheck.net/blog/state-of-open-source-local-llms-october-2026/
- https://prismix.dev/news/a0c0ec2a914c
Last updated Oct 24, 2026. Fields tagged unverified lack a primary source. "No published figure yet" means we found no source, and we don't guess. Spark fit is estimated from published memory sizes, not tested on Spark hardware. Ollama listings may be cloud-only tags; check this for Kimi K3 and DeepSeek V4.1 before pulling.
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