An open-source 9B model with a 1M-token context and an Apache 2.0 license has just been released on Hugging Face. It’s designed to run on a single consumer-class GPU, such as the NVIDIA RTX 5090. This model breaks the "Compute Wall" and the "Memory Wall," achieving 3.5× faster inference and significantly lower KV-cache overhead compared to dense baselines. This is no longer an "either-or" choice between performance and efficiency. How? Full Attention mechanism's computational complexity grows quadratically with length, making edge-side long-text inference "slow and memory-intensive." Solution: MiniCPM-SALA adopts a golden ratio of 75% Linear Attention + 25% Sparse Attention. MiniCPM-SALA (9B) is OpenBMB’s long-context model aimed at running 1M to 2M tokens on a single GPU without the memory spikes and OOM failures common with dense full attention. The main idea is a sparse plus linear hybrid that keeps long-range recall accurate while keeping cost manageable as context grows. - Architecturally, about 25% of layers use InfLLM-V2 style sparse attention for high-fidelity long-range retrieval, while about 75% use Lightning linear attention, so compute scales close to linearly with sequence length. Instead of a uniform interleave, the sparse layers are placed via a 1:3 layer-selection pattern. - For positional handling and stability, SALA uses hybrid positional encoding (HyPE): RoPE stays in the linear layers but is removed in sparse layers to avoid long-range decay, and it adds QK-normalization plus output gating to improve stability and reduce attention-sink behavior. - Training is done by converting a pretrained Transformer, not training from scratch. It starts from a MiniCPM-4.0 intermediate checkpoint trained on 7T tokens, then applies HALO conversion, keeping the 1st and last layers unconverted and initially training only the converted linear layers. Conversion plus post-training totals about 2T tokens, framed as about a 75% cost reduction versus an 8T scratch run, with context ramping from 512 to 4K, then to 32K, 160K, and 520K, followed by SFT at 64K and 140K. Reported results keep standard performance strong (76.53 average, HumanEval 95.12, AIME24 83.75, AIME25 78.33) While improving long-context behavior (RULER 92.65 at 64K, 89.37 at 128K). It also reports single-GPU 1M-token inference where Qwen3-8B OOMs, 256K TTFT, improving from 180.8s to 51.6s, and RULER holding at 86.3 at 1M and 81.6 at 2M without YaRN. Go to Hugging Face/GitHub to test the model capabilities yourself.