Majestic Labs vs. the Memory Wall

On November 10, 2025, three former Google and Meta silicon executives announced they’ve raised $100 million to build what they’re calling a fundamentally different kind of AI server. Not faster chips. Not more GPUs. More memory orders of magnitude more packed into a single box that could replace entire racks of today’s hardware (CNBC, Nov 10).

Majestic Labs’ pitch is simple: the bottleneck in AI inference isn’t compute anymore. It’s memory. Specifically, the fixed compute-to-memory ratio that every GPU ships with and the KV cache bloat that comes free with every long-context request.

Key context:

• Majestic Labs: $100M raised (Series A led by Bow Wave Capital, Sept 2025); founders Ofer Shacham (CEO, ex-Google/Meta silicon lead), Sha Rabii (President, ex-Google Argos video chip lead), Masumi Reynders (COO, ex-Google TPU biz dev). Claims patent-pending architecture delivers 1,000× typical server memory. Prototypes target 2027 (CNBC, Nov 10)
• Global AI capex surge: Alphabet $91–93B (2025), Meta ≥$70B, Microsoft $34.9B in Q3 alone (+74% YoY), Amazon ~$118B (TrendForce, Oct 30)
• vLLM PagedAttention: 2–4× throughput vs state-of-the-art at same latency; achieves near-zero KV cache waste (arXiv, Sept 2023)
• CXL memory pooling: 100 TiB commercial pools available in 2025; XConn/MemVerge demo showed >5× performance boost for AI inference vs SSD (AI-Tech Park, Oct 2025)

The memory wall isn’t new, but the scale is

You feel it first as a ceiling, not a wall. Batch a few more requests and tokens-per-second look great until you stretch the context or let tenant count creep up. Suddenly the GPU says no. Not because FLOPs tapped out. Because memory did.

“Nvidia makes excellent GPUs and has driven incredible AI innovation. We’re not trying to replace GPUs across the board we’re solving for memory-intensive AI workloads where the fixed compute-to-memory ratio becomes a constraint.”

Ofer Shacham, Majestic Labs CEO (CNBC, Nov 10)

Translation: inference is a KV-cache business. Every token you generate requires storing attention keys and values for every previous token in the sequence. Increase context length and memory grows quadratically. Serve multi-tenant RAG and your index footprints follow you into VRAM. Disaggregate prefill and decode and now you’re passing state across workers which means duplicating it or bottlenecking on fabric.

The cheapest way to buy back throughput is often not “more compute.” It’s more room.

Software has done heroic work to bend this curve. vLLM’s PagedAttention achieves near-zero KV cache waste by borrowing virtual memory tricks from operating systems, delivering 2–4× higher throughput than prior systems at the same latency (arXiv, Sept 2023). NVIDIA’s open-source Grove (part of Dynamo) popularized disaggregated prefill/decode workers so you can scale the hot path without over-provisioning the cold one (NVIDIA Developer Blog, Nov 2025). And CXL memory pooling moved from “interesting research” to 100 TiB commercial deployments in 2025, with demos showing >5× performance boost for AI workloads vs SSD-backed memory (AI-Tech Park, Oct 2025).

Still, the physics are stubborn. HBM ships in fixed ratios. Datacenter memory is expensive and fragmented. The only way to get “more room” today is to scale horizontally add more nodes, duplicate state, pay network tax.

Majestic is betting that flipping the ratio at the box level changes the game. If each server carries 1,000× typical memory (their claim), you consolidate footprint, reduce duplication, and push batch/context limits higher without paying OOM tax.

Prototypes won’t land until 2027. Bandwidth, latency, fabric integration, and TCO will determine whether this is a real shift or just a bigger box. But the thesis is grounded: memory-bound workloads are real, growing, and under-served by today’s hardware.

What a T4 tells us about the slope

We ran a small vLLM benchmark on Google Colab (Tesla T4 16GB) to make the memory-throughput tradeoff concrete. Not production scale, just the shape of the curve.

Setup:

• Hardware: Tesla T4 (16GB VRAM, Compute Capability 7.5)
• Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 (max_model_len=2048, derived from model config)
• Backend: vLLM with TORCH_SDPA attention (fp16 fallback), gpu_memory_utilization=0.70
• Test grid: context lengths {512, 1024, 2048} tokens × batch sizes {1, 4}
• Generation: 32 tokens per request, 3 iterations per config

Results:

Key observations:

1. Batch scales throughput hard. Single-request runs deliver 4.57–26.85 tok/s. Batch 4 jumps to 80–98 tok/s. That’s a 3.6–21× multiplier depending on context length.

2. Long context taxes throughput and memory. At batch 4, going from 512 → 2048 tokens drops TPS from 98.43 → 80.27 (-18%), while GPU memory climbs ~390 MiB. The KV cache is visible in the numbers.

3. Latency stays reasonable but creeps up. Median end-to-end for 32 tokens ranges 1.19–1.59s. P99 was 1.36–1.61s (not shown in table). This is a small model on modest hardware, so the absolute numbers are forgiving, but the slope is there.

This is exactly where Majestic’s thesis lands. If you had 10× or 100× the memory per box, you could push batch and context higher without the OOM cliff. Long-context multi-tenant inference the stuff that’s memory-bound today gets headroom to breathe. The TPS-per-server number climbs, and you consolidate footprint instead of scaling horizontally and paying network tax.

It’s a small test on a small model. But the curve is the curve. Memory limits batch. Batch limits throughput. More memory buys you more throughput per box for the workloads that matter.

Resources:

• Majestic Labs raises $100M for high‑memory AI servers; prototypes targeted for 2027 (CNBC, Nov 10)
• vLLM PagedAttention paper (arXiv, Sept 2023)
• vLLM blog: PagedAttention explained
• NVIDIA Grove: Streamline complex AI inference on Kubernetes (Nov 2025)
• CXL memory pooling demo: XConn/MemVerge show >5× boost for AI (AI-Tech Park, Oct 2025)
• CXL Consortium: Expanding your memory footprint with CXL at FMS 2025

Further Reading:

• China’s AI Dual Flywheel: Why Mainland Hardware Wins First, Hong Kong Internet Later
• Meta’s $75B AI Infrastructure Bet
• DeepSeek-OCR: When a Picture Is Worth 10× Fewer Tokens

Connect

• GitHub: @0xReLogic
• LinkedIn: Allen Elzayn