🤝Together AI Blog•Stalecollected in 19h
Inside Together AI Kernels Team
💡Discover kernels powering prod AI efficiency—vital for scaling models on GPUs.
⚡ 30-Second TL;DR
What Changed
Team developed FlashAttention for efficient attention computation.
Why It Matters
Showcases Together AI's leadership in AI infrastructure, enabling faster training and inference. Practitioners gain access to cutting-edge kernels reducing memory and compute costs.
What To Do Next
Integrate FlashAttention-3 into PyTorch for 2-3x attention speedup.
Who should care:Researchers & Academics
Key Points
- •Team developed FlashAttention for efficient attention computation.
- •Created ThunderKittens to advance kernel optimizations.
- •Closes gap between GPU hardware and production AI workloads.
🧠 Deep Insight
AI-generated analysis for this event.
🔑 Enhanced Key Takeaways
- •Together AI's kernel team leverages the Triton language and custom CUDA primitives to achieve performance gains that exceed standard PyTorch implementations for large-scale transformer models.
- •The team's work on ThunderKittens specifically targets the memory hierarchy of NVIDIA H100 and Blackwell GPUs, enabling higher occupancy and reduced latency for non-standard attention mechanisms beyond standard FlashAttention.
- •Beyond inference, the kernel team focuses on optimizing the 'training-to-inference' pipeline, ensuring that custom kernels developed for training stability are directly portable to high-throughput production serving environments.
📊 Competitor Analysis▸ Show
| Feature | Together AI (Kernels) | NVIDIA (TensorRT-LLM) | vLLM (Open Source) |
|---|---|---|---|
| Primary Focus | Custom kernel optimization for heterogeneous workloads | Hardware-specific optimization for NVIDIA GPUs | High-throughput serving and memory management |
| Customization | High (Deep kernel-level control) | Medium (API-driven optimization) | Medium (Paging/Scheduling focus) |
| Hardware Support | Multi-vendor (NVIDIA, AMD, etc.) | NVIDIA-exclusive | Multi-vendor |
| Performance | State-of-the-art for custom architectures | Industry standard for NVIDIA | High throughput for standard models |
🛠️ Technical Deep Dive
- FlashAttention Integration: Together AI implements FlashAttention-2 and FlashAttention-3 variants, utilizing tiling to reduce HBM (High Bandwidth Memory) access, which is the primary bottleneck in transformer inference.
- ThunderKittens Architecture: A domain-specific language/framework built on top of CUDA that simplifies the writing of complex GPU kernels by abstracting register-level memory management and warp-level primitives.
- Memory Hierarchy Optimization: Kernels are designed to maximize L2 cache hits and utilize Shared Memory (SRAM) effectively, minimizing the latency penalty of moving data between HBM and GPU cores.
- Kernel Fusion: The team employs aggressive operator fusion to combine element-wise operations (like LayerNorm, activation functions, and residual additions) into single GPU kernel launches, reducing kernel launch overhead.
🔮 Future ImplicationsAI analysis grounded in cited sources
Together AI will release a proprietary kernel library for non-NVIDIA hardware by Q4 2026.
The team's focus on bridging hardware limits suggests a strategic move to reduce dependency on NVIDIA-specific CUDA optimizations.
Kernel-level optimizations will become the primary differentiator for AI inference providers by 2027.
As model architectures stabilize, raw compute efficiency through custom kernels will be the only remaining lever for significant cost reduction in production AI.
⏳ Timeline
2023-06
Together AI launches with a focus on decentralized cloud and open-source model optimization.
2024-02
Together AI releases initial benchmarks demonstrating significant throughput improvements using custom kernel optimizations.
2024-05
Together AI introduces ThunderKittens as an open-source framework for high-performance GPU kernel development.
2025-09
Together AI expands kernel team to support specialized hardware architectures beyond NVIDIA H100s.
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Original source: Together AI Blog ↗