Marktechpost AI on X: "Thinking Machines Lab released Inkling-Small on July 30, 2026. It is a 276B total, 12B active Mixture-of-Experts model under Apache 2.0, roughly a quarter the size of Inkling. 1. The smaller model passed its teacher → SWE-bench Verified: 80.2% vs Inkling's 77.6% → Terminal-Ben
Quick Answer
Thinking Machines Lab launched Inkling-Small, a 276B total, 12B active Mixture-of-Experts model, achieving benchmark scores of 80.2% on SWE-bench, outperforming its predecessor Inkling.
Quick Take
The model's sparsity allows it to fit on a single GPU, making it efficient for deployment.
Key Points
- Inkling-Small is a 276B total, 12B active model under Apache 2.0.
- Achieved 80.2% on , surpassing Inkling's 77.6%.
- Utilizes 42 decoder layers with 4.3% weights active per token.
- Fits on a single GPU with 600 GB aggregated VRAM for BF16 checkpoint.
- Started training after Inkling, allowing for improved pre-training data mix.
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~1 min readThinking Machines Lab released Inkling-Small on July 30, 2026. It is a 276B total, 12B active Mixture-of-Experts model under Apache 2.0, roughly a quarter the size of Inkling. 1. The smaller model passed its teacher → SWE-bench Verified: 80.2% vs Inkling's 77.6% → Terminal-Bench 2.1: 64.7% vs 63.8% → HLE, text only: 31.6% vs 29.7% → ARC-AGI-2: 40.1% vs 36.5% Inkling-Small started training after Inkling. That let the team revise the pre-training data mix, distill from Inkling on-policy, then scale agentic coding RL for two more weeks. 2. Sparsity is doing the work → 42 decoder layers, hybrid local and global attention → 6 of 256 experts routed per token, plus 2 shared experts → ~4.3% of weights active on any given token → 1M token context window 3. It fits on one GPU → BF16 checkpoint: 600 GB aggregated VRAM (4x B300 or 8x H200) → NVFP4 checkpoint: 180 GB, W4A4 on 1x B300 (SM100+) or W4A16 on 2x H200 → Runtimes: SGLang, vLLM, TokenSpeed, Unsloth, Hugging Face Full analysis: marktechpost.com/2026/08/02/thi… Technical details: thinkingmachines.ai/news/inkling-s… Model weights: huggingface.co/thinkingmachin…
@thinkymachines— Originally published at x.com
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