Inkling
Summary: Thinking Machines Lab's 975B total / 41B active parameter open-weight multimodal MoE model (Apache 2.0) with hybrid local/global attention, short convolutions, embedding RMSNorm, and learned relative-position bias instead of RoPE. 1M token context, supports BF16/MXFP8/NVFP4.
Details
Architecture
- Type: Multimodal autoregressive transformer (decoder-only)
- Layers: 66 decoder layers
- Attention: Hybrid local/global
- 55 local attention layers: 512-token sliding window
- 11 global attention layers: full attention (learned bias applied to preceding 1024 tokens only)
- MoE: Sparse Mixture-of-Experts
- 256 experts, top-6 routing per token
- 2 shared experts (always active)
- 4.2% parameter activation per token (41B/975B)
- Positional Encoding: Learned input-dependent relative-position bias (no RoPE)
- Convolutions: Kernel-4 convolutions after K/V projections and on attention/MLP branch outputs
- Normalization: Embedding RMSNorm (after token embeddings, separate from per-block pre-attention RMSNorm)
Parameters
- Total: 975B
- Active: 41B
- Context window: 1M tokens
- Modalities: Text, Image, Audio → Text output
Quantization & Deployment
- BF16: 2 TB VRAM (8× B300 or 16× H200)
- NVFP4 (W4A4): 600 GB VRAM (4× B300 SM100+, or 8× H200 W4A16)
- Frameworks: SGLang, vLLM, TokenSpeed, Unsloth, Hugging Face Transformers
- Weights: Hugging Face (thinkingmachines/inkling)
- API: Tinker platform + third-party inference providers
Benchmarks (Model Card)
| Category | Benchmark | Inkling | Top Closed |
|---|---|---|---|
| Reasoning | HLE (no tools) | 29.7% | GPT 5.6 Sol 47.2% |
| Reasoning | HLE (with tools) | 46.0% | GPT 5.6 Sol 55.0% |
| Reasoning | AIME 2026 | 97.1% | GPT 5.6 Sol 99.9% |
| Reasoning | GPQA Diamond | 87.2% | GPT 5.6 Sol 94.1% |
| Coding (agentic) | SWE-Bench Verified | 77.6% | Claude Fable 5 95.0% |
| Coding (agentic) | SWE-Bench Pro Public | 54.3% | Claude Fable 5 80.0% |
| Coding (agentic) | Terminal Bench 2.1 | 63.8% | GPT 5.6 Sol 89.5% |
| Agentic (general) | GDPVal-AA v2 | 1238 | Claude Fable 5 1760 |
| Agentic (general) | MCP Atlas | 74.1% | Claude Fable 5 83.3% |
| Factuality | SimpleQA Verified | 43.9% | Gemini 3.1 Pro 77.3% |
| Vision | MMMU Pro Standard 10 | 73.5% | Gemini 3.1 Pro 82.0% |
| Vision | CharXiv RQ (with Python) | 82.0% | GPT 5.6 Sol 87.8% |
| Audio | VoiceBench | 91.4% | Gemini 3.1 Pro 94.3% |
| Safety | FORTRESS Adversarial | 78.0% | Claude Fable 5 96.0% |
Significance
Honest, broad benchmark profile — not benchmark-specialized. Interesting architectural departures from DeepSeek-V3/Kimi recipe: (1) short convolutions for local inductive bias, (2) embedding RMSNorm (unusual placement), (3) learned relative-position bias instead of RoPE (claim better extrapolation), (4) less extreme sparsity (4.2% vs 3.2%). Designed for fine-tuning via Tinker platform. Open weights (Apache 2.0) enables independent research.
Related Concepts
- inkling-architecture
- embedding-rmsnorm
- relative-position-bias
- short-convolutions
- learned-relative-position-bias
- nope-attention
- thinking-machines-lab
- tinker-platform
- moe-sparsity-tradeoffs
Sources
- inkling-model-card: Model Properties — 975B/41B, 66-layer, hybrid local(55)/global(11) attention, 256 experts top-6 + 2 shared, 1M context
- inkling-model-card: Numerics Support — BF16/MXFP8/NVFP4; NVFP4: 600GB VRAM (4×B300 or 8×H200)
- inkling-model-card: Benchmarks — SWE-Verified 77.6%, Terminal Bench 63.8%, IFBench 79.8%, SimpleQA 43.9%, HLE no tools 29.7%
- inkling-raschka-blog: Architecture observations — Kernel-4 convolutions, embedding RMSNorm, learned relative-position bias (no RoPE)
- inkling-raschka-blog: Comparison — 4.2% active params vs Kimi K2.5's 3.2%; regular decoder vs Mamba-hybrid