Release Notes

Release 1.8.0

Summary

This release replaces the separate TensorRT and PyTorch backend options with a unified optimized backend built on new custom kernels, improves inference performance and supported sequence lengths, and adds affinity embedding outputs for binding affinity prediction.

Key Features

• Unified optimized backend: Removed the previous separate TensorRT (trt) and PyTorch (torch) backend selection. The NIM now uses a single new optimized backend that automatically selects the best execution path for each request.

• Automatic backend selection:

  • Fully supports the Boltz2 OSS model
  • Short sequences (≤768 residues): TensorRT engines
  • Medium and long sequences (>768 residues): PyTorch backend with new optimized kernels

• New optimized kernel backend includes:

• Fused Adaptive Layer Norm

• DualGemm sm80 (two variants)

• Gated sigmoid sm80

• FAv2/v3 TriangleAttent/AttentionPairBias

• Pre-computed masks, with masks left-aligned into new kernels for dual GEMM and attention operations

• Pre-computed buffers for Pairformer/DiT

• Performance increased up to 1.7x on H100, and sequence length limits increased across supported GPUs:

  GPU family	Previous max sequence length	New max sequence length	Increase
  A100 / H100	2048	2560	25%
  L40S / A6000 / RTX 6000 Ada	1536	1665	8.3%
  B200 / B300 (Blackwell)	2048	3072	50%

  Actual maximum supported sequence length may exceed the values shown above, depending on available GPU VRAM.

• Affinity embedding outputs: Boltz2Affinity now exports embedding vectors as outputs. The new ligand-level request flag output_affinity_embedding defaults to false and requires predict_affinity=true on the same ligand. When enabled, the ligand’s entry under affinities includes affinity_embedding, model_1_affinity_embedding, and model_2_affinity_embedding (2D number arrays or null). Each embedding vector has 384 dimensions.

Removed Configuration

The following environment variables are no longer supported and have been removed:

• NIM_BOLTZ_ENABLE_DIFFUSION_TF32
• NIM_BOLTZ_STRUCTURE_OPTIMIZED_BACKEND
• NIM_BOLTZ_AFFINITY_OPTIMIZED_BACKEND

Notes

• For best performance with the unified optimized backend, run a warm-up pass with representative test-case sequences before measuring latency/throughput.

• The first request in a given sequence-length range can be slower because of JIT compilation and runtime kernel specialization. As a result, you may observe a one-time startup latency when entering a new range (for example, short, medium, or long sequences). After that initial compilation for the range, subsequent requests in the same range should show improved performance.