Rubin, custom AI chips and compressed KV caches
Illustrative hard-drive interior, showing storage hardware rather than GPU memory.
The next wave of AI infrastructure pushes beyond single-chip scaling into rack-scale co-designed supercomputers, domain-specific ASIC architectures, and sub-byte KV cache compression.
1. NVIDIA Rubin (R100) & Next-Gen AI Hardware
Shipping in late 2026, the NVIDIA Vera Rubin platform transitions from a single-chip refresh into a six-chip rack-scale architecture built for agentic AI and reasoning models. [1], [2], [3], [4]
- Rubin GPU (3nm & Third-Gen Transformer Engine). Built on TSMC’s 3nm process with 336 billion transistors and 224 Streaming Multiprocessors, a single Rubin GPU delivers 50 PFLOPS of sparse NVFP4 inference compute and 35 PFLOPS of NVFP4 training compute—a 3.3× to 5× leap over Blackwell. [1], [2], [3]
- HBM4 Memory Subsystem. Rubin replaces HBM3e with 288 GB of HBM4 memory, delivering 19.2 TB/s of memory bandwidth per GPU in final shipping configurations (more than doubling Blackwell's 8 TB/s). This massive bandwidth directly accelerates memory-bound LLM decode steps and multi-million-token context windows. [1], [2], [3], [4]
- NVLink 6 & Rack-Scale NVL72. Sixth-generation NVLink 6 provides 3 TB/s of bidirectional GPU-to-GPU bandwidth per chip (and 216 TB/s aggregate across a 72-GPU Vera Rubin NVL72 rack). Paired with the new Vera CPU (88 custom Armv9.2 Olympus cores connected via 1.8 TB/s NVLink-C2C), the entire 72-GPU rack functions as a single unified computer. [1], [2], [3]
2. Custom AI Accelerators vs. NVIDIA CUDA GPUs
While NVIDIA GPUs act as flexible parallel processors capable of running graphics, scientific simulations, and any AI architecture, custom ASICs (Application-Specific Integrated Circuits) strip away general-purpose hardware to maximize matrix math efficiency. [1]
| Dimension | NVIDIA GPU (CUDA) | Google TPU (v5p / Trillium) | AWS Trainium / Inferentia | Groq LPU (Language Processing Unit) |
|---|---|---|---|---|
| Core Compute Design | Thousands of SIMT cores + Tensor Cores managed dynamically by hardware schedulers. | Systolic Arrays (2D grids of ALUs that pump data rhythmically without register reads). | NeuronCores (systolic matrix engines paired with programmable SIMD vector engines). | Deterministic VLIW (Very Long Instruction Word) single-core spatial architecture. |
| Memory Architecture | Massive off-chip HBM3e / HBM4 (80 GB–288 GB) paired with small on-chip SRAM caches. | High-capacity HBM paired with large software-managed scratchpad memory (VMEM). | HBM + large on-chip SRAM caches optimized for compiler-scheduled data reuse. | Zero external HBM. Uses on-chip SRAM only (~230 MB per chip) delivering ~80 TB/s bandwidth. |
| Scheduling & Execution | Hardware-scheduled at runtime (warps dynamically hide memory latency). | Compiler-scheduled statically via XLA (Accelerated Linear Algebra). | Compiler-scheduled statically via the AWS Neuron SDK. | 100% deterministic cycle-accurate scheduling by the Groq compiler. |
| Key Trade-Off | Unmatched software ecosystem (CUDA) and flexibility, but higher cost and power draw. | Exceptional multi-thousand-chip scaling (ICI torus interconnect), tied to Google Cloud/JAX. | Strong price-performance on AWS, requires Neuron compiler tuning for custom ops. | Ultra-low token latency (hundreds of tokens/sec), but requires many chips to hold large models. |
3. KV Cache Compression Techniques
As reasoning models generate tens of thousands of thinking tokens and users feed entire codebases into prompts, the Key-Value (KV) Cache grows to tens of gigabytes per user—becoming the primary bottleneck for batch size and inference speed.
- Multi-Head Latent Attention (MLA) – DeepSeek.
- The Standard MHA/GQA Bottleneck. Standard Multi-Head Attention (MHA) stores full-dimension Key and Value vectors for every single attention head and token. Even Grouped-Query Attention (GQA) still caches large uncompressed matrices.
- Low-Rank Joint Compression. Introduced in DeepSeek-V2 and scaled in DeepSeek-V3/R1, MLA projects the hidden state down into a tiny, shared compressed latent vector (c_(t)^(KV), e.g., 512 dimensions instead of 16,384+) and caches only that compact latent vector in VRAM.
- Matrix Absorption Trick. During inference, instead of decompressing the latent vector back into huge K and V matrices in VRAM, MLA mathematically absorbs the decompression projection matrices directly into the Query (W_(Q)) and Output (W_(O)) weight matrices. This cuts KV cache memory by 90%+ while outperforming standard MHA in accuracy.
- FP8 & INT4 KV Cache Quantization.
- Storing KV caches in standard 16-bit precision (FP16/BF16) wastes half the available memory bandwidth during decode. Modern serving engines (TensorRT-LLM, vLLM) quantize K and V tensors on the fly into FP8 (E4M3) or INT4 as new tokens are generated.
- Because Key tensors often contain extreme outlier values across specific feature channels, kernels apply per-channel scaling to Keys and per-token scaling to Values, cutting KV cache memory footprint and HBM read time in half with virtually zero accuracy degradation.
- Token Eviction & Sliding Window Compression (H2O / SnapKV).
- Research shows that over 95% of attention scores concentrate on a tiny fraction of tokens: the very first few tokens ("Attention Sinks" that stabilize Softmax probabilities) and the most recent local tokens.
- Dynamic eviction algorithms like H2O (Heavy-Hitter Oracle) and SnapKV continuously track cumulative attention scores, keeping only Attention Sinks, a sliding window of recent tokens, and high-importance "heavy hitter" tokens while evicting low-impact middle tokens from VRAM.