TensorFold Makes Local AI 2x Faster

This is a meaningful inference-engine breakthrough, not a faster model: TensorFold extracts more throughput from existing weights without changing model quality.

• –Qwen3.8 Flash Next reaches over 100 tok/s across two DGX Sparks, while M3 Ultra results exceed 105 tok/s.
• –TensorFold reports roughly 1.6–3.1x gains over vLLM with MTP and DFlash2 recipes.
• –Exact, byte-identical decoding makes speculative acceleration easier to trust in reproducible developer workflows.
• –Results depend heavily on model family, prompt type, quantization, and hardware configuration.
• –Single-user speed is the headline; high-concurrency serving can still favor mature engines such as vLLM.