SpikingBrain Models: Efficient, Sparse LLMs

• SpikingBrain models are brain-inspired neural architectures that integrate adaptive spiking neurons, hybrid linear and sliding window attention, and event-driven processing for efficient long-context inference.
• They employ a conversion-based training pipeline that adapts open-source Transformers into spiking versions using continual pre-training over 150B tokens and recursive linear attention approximations.
• Their design optimizes energy efficiency and speed, achieving >100× time to first token improvements on ultra-long sequences while maintaining competitive accuracy and constant-memory operation.

SpikingBrain models are a class of large-scale, brain-inspired neural architectures that integrate event-driven spiking mechanisms and energy-efficient attention modules for efficient long-context training and inference. Developed to address the computational and engineering bottlenecks of Transformer-based LLMs—especially on non-NVIDIA platforms—SpikingBrain models adopt adaptive spiking neurons, linear and hybrid-linear attention, and customized training frameworks to achieve highly sparse, constant-memory, low-power operation with competitive performance on standard and long-sequence benchmarks (Pan et al., 5 Sep 2025).

1. Architecture of SpikingBrain Models

SpikingBrain incorporates two flagship models: SpikingBrain-7B and SpikingBrain-76B. Both employ event-driven spiking mechanisms as a core computational element.

• SpikingBrain-7B utilizes a linear attention-based LLM design with interleaved layers that alternate between linear attention and sliding window attention (SWA). Linear attention achieves constant memory by recurrently computing the attention state, while the SWA branch targets local dependencies through a fixed window.
• SpikingBrain-76B extends the design with a hybrid approach at the intra-layer level. Each layer computes multiple attention branches in parallel: linear attention, SWA, and in select layers, full softmax attention. Feed-forward modules include a large Mixture-of-Experts (MoE) configuration, activating only a minority of experts per token. Seven dense FFN layers are always active for stability; other FFN layers are sparsified in MoE format.

Both models feature adaptive spiking neurons. In these, a spiking conversion layer computes the firing threshold for every token as $V_{th}(\mathbf{x}) = \text{mean}(|\mathbf{x}|)/k$. After temporal collapse (during training), a continuous activation $v_T$ is quantized to an integer spike count $$s_{\mathrm{INT}} = \text{round}(v_T / V_{th}(\mathbf{x}))$$. This dynamic thresholding ensures event-driven sparsity in both training and inference.

2. Algorithmic Optimization: Conversion-Based Pipeline and Spike Coding

SpikingBrain adopts a conversion-based training pipeline. Instead of training from scratch, an open-source Transformer (e.g., Qwen2.5-7B-base) is converted to linear and SWA attention via two equivalences:

• Softmax attention $A = \text{softmax}(QK^\top \odot M)$ is approximated as either a strictly local (sliding window) attention with a masked $M'$, or a low-rank approximation (linear attention) implemented recursively: $o_t = q_t \cdot S_t$, $S_{t} = S_{t-1} + k_t^\top v_t$.

Transfer is effected via continual pre-training over $\sim$150B tokens (substantially fewer than the standard $\sim$10T in LLM pipelines), progressively increasing the sequence length (from 8k to 128k).

Spike coding in inference occurs in two stages:

1. Accumulated activations are collapsed to integer spike counts in a single step.
2. These counts are “expanded” into temporally sparse spike trains using one of three strategies:
   • Binary (1/0) coding
   • Ternary (–1, 0, 1) coding, which permits inhibitory and excitatory spiking
   • Bitwise coding, where counts are unpacked into binary bits for maximal sparsity.

This enables efficient, event-driven computation on hardware architectures that capitalize on sparse, asynchronous execution.

3. System Engineering: MetaX Hardware and Operator Design

SpikingBrain is specifically engineered for the MetaX GPU cluster—a non-NVIDIA, highly parallel hardware environment. Custom operator libraries and parallelization strategies are required:

• Operator Design: Kernels are adapted via Triton JIT compilation and then migrated to the internal MACA (MetaX Accelerator) system, requiring handling of cache hierarchies and vectorized instructions.
• Parallelism: Training leverages composite parallelization: data parallelism (DP), pipeline parallelism (PP), expert parallelism (EP, for MoE layers), and sequence parallelism (SP). Memory optimizations such as ZeRO and activation recomputation are also employed.

These techniques yield stable training over multi-week runs and hundreds of GPUs, as evidenced by a Model FLOPs Utilization (MFU) of 23.4% for SpikingBrain-7B.

4. Performance: Efficiency, Accuracy, and Scalability

SpikingBrain models achieve notable hardware and algorithmic efficiency:

• SpikingBrain-7B maintains nearly 90% of its base Transformer model’s accuracy on standard benchmarks such as MMLU, CMMLU, ARC-C, HellaSwag, and Ceval.
• Long-sequence inference benefits from dramatic improvement: the 7B model delivers >100× speedup in Time to First Token (TTFT) for 4M-token prompts compared to quadratic-complexity Transformers.
• SpikingBrain-76B (hybrid-linear MoE) matches or outperforms Llama2-70B, Mixtral-8×7B, and Gemma2-27B on select benchmarks. Its intra-layer hybrid attention and sparse MoE design ensure both high throughput and constant memory in the long-context regime.
• Training scalability is achieved with continual pre-training on only ~150 billion tokens, a major reduction compared to conventional LLMs.

5. Sparsity and Power Efficiency

Sparsity is a major differentiator in SpikingBrain’s computational footprint.

• Neuron- and Channel-Level Sparsity: In the 7B model, 18.4% of channels are inactive; after spike-expansion, overall sparsity reaches ≈69.15%.
• Energy Efficiency: Standard FP16 multiply-accumulate (MAC) operations consume ≈1.5 pJ; INT8 MAC is ≈0.23 pJ. The spiking paradigm, using only spike-triggered additions (≈0.03 pJ per INT8 addition and ≈1.13 average spike count per activation), yields an effective MAC energy ≈0.034 pJ—up to 97.7% lower than FP16.
• Compatibility with neuromorphic inference: Sparse spike trains allow asynchronous, event-driven hardware operation, further reducing static energy loss and latency.

6. Comparisons and Limitations

Compared to classic Transformer-based LLMs, SpikingBrain models introduce:

• Near-linear attentional complexity using hybrid attention architectures, replacing quadratic softmax mechanisms.
• Event-driven sparse update semantics, reducing computational and memory load for ultra-long sequences.
• Adaptive spiking neurons that dynamically control firing rates for optimal code efficiency, whereas most traditional LLMs use fixed nonlinearities (e.g., GELU).

Inference in ultra-long contexts is delivered with (partial) constant memory, and adaptive spiking neurons maintain stable training dynamics despite limited firing.

A plausible implication is that, by combining the energy efficiency, adaptive sparsity, and high throughput of brain-inspired spiking mechanisms with advanced attention architectures, SpikingBrain models constitute a scalable and power-efficient path forward for long-context and hardware-constrained large model training (Pan et al., 5 Sep 2025). However, while performance matches or approaches leading open-source LLMs, full-parity with state-of-the-art, full-attention models remains a topic for further research. The use of approximate attention and MoE sparsity introduces trade-offs in local dependency modeling, and the event-driven coding paradigm sometimes incurs a minor accuracy cost on standard benchmarks in exchange for significant gains in memory and latency.

Summary Table: Key Features of SpikingBrain Models

Aspect	SpikingBrain Implementation	Impact
Attention Mechanism	Linear/Sliding Window/Hybrid Softmax	O(n) time and memory
Neuron Type	Adaptive Threshold Spiking	High sparsity, event-driven
Training Pipeline	Conversion-based continual pre-training	Token-efficient, stable
Hardware Deployment	MetaX GPUs, MACA & Triton operators	Non-NVIDIA, scalable
Inference Speedup	>100× TTFT on 4M sequences (7B model)	Ultra-long sequence support
Model Sparsity	≈69% overall spike-level	Energy savings, low power
Benchmark Performance	~90% base model (7B), parity (76B MoE)	Competitive accuracy

These features position SpikingBrain as a leading brain-inspired solution for large-scale, efficient, and scalable LLMing, especially in settings where memory, latency, and power constraints are dominant considerations.