128GB Unified Memory vs. Discrete VRAM: What It Means for Legal AI

If you've looked at AI hardware specifications, you've probably seen numbers like "24GB VRAM" or "48GB VRAM." These refer to the dedicated memory on graphics cards used for AI processing. For most consumer and business applications, 24GB is generous. For running the smartest legal AI? It's a crippling limitation.

Why Memory Matters: It's About Model Size

Here's what most vendors won't tell you: memory determines what model you can run, not how many pages you can analyze. A 70-billion parameter model (70B)—the kind that can reason like a senior attorney—requires approximately 40GB of VRAM at 4-bit quantization. That's more than any consumer GPU offers.

The practical implications:

24GB VRAM (RTX 4090): Limited to 8B models—fast but less sophisticated
32GB VRAM (RTX 5090): Still limited to 8B models—faster, same reasoning limit
128GB Unified Memory (GB10): Runs 70B models natively—the "Senior Partner" brain

Two Types of AI Memory

Legal AI actually uses two distinct types of memory, and confusing them leads to misleading marketing claims:

Searchable Memory (RAG)

This is your document library. Using vector search, the AI can retrieve relevant passages from millions of pages in milliseconds. This scales essentially without limit—50,000 pages, 500,000 pages, it doesn't matter. All LegalVault units support massive RAG databases.

Active Memory (Context Window)

This is what the AI reads simultaneously. Current state-of-the-art models like Llama 3.1 have a 128,000 token context limit—that's approximately 200-250 pages of dense legal text that the AI can reason about at once.

This is a model limitation, not a hardware one. Even with 1TB of memory, you can't exceed what the model architecture supports.

The 8B vs 70B Intelligence Gap

So if active memory is model-limited, why does hardware memory matter? Because it determines which model you can run:

8B Models (Junior Associate)

Fast execution: 150-200+ tokens per second
Great for: Contract drafting, summarization, routine review
Limitation: May miss nuanced legal arguments
Runs on: Consumer GPUs (RTX 5090)

70B Models (Senior Partner)

Slower generation: ~45 tokens per second
Great for: Complex analysis, finding contradictions, strategic reasoning
Strength: Catches issues 8B models miss
Requires: 128GB unified memory or 96GB+ discrete VRAM

Think of it this way: the 8B model is a brilliant first-year associate who works incredibly fast. The 70B model is the partner who's seen everything and catches the issue the associate missed.

What Unified Memory Actually Enables

NVIDIA's Grace Blackwell architecture—the chip inside LegalVault's Spark—takes a fundamentally different approach. Instead of discrete GPU memory that's separate from system RAM, it uses unified memory: a single, large memory pool shared between CPU and GPU.

The technical advantages:

128GB total capacity: Enough to run 70B models comfortably
No memory transfer bottleneck: Data doesn't need to be copied between CPU and GPU memory
Coherent access: Both processors can work on the same data simultaneously
Efficient large-model inference: The architecture is optimized for exactly this use case

Real-World Performance

What does this mean in practice? Consider a typical M&A document review:

Target company contracts: 200 documents
Average length: 15 pages each
Total: 3,000 pages

Here's how LegalVault handles this with a 70B model on the Spark:

All 3,000 pages indexed into searchable memory (RAG)
Relevant sections retrieved based on your query
~250 pages loaded into active context for analysis
70B reasoning applied to find patterns, contradictions, and risks

You can ask:

"Identify all change of control provisions across the document set and flag any that would be triggered by this acquisition."

The AI searches everything, loads the most relevant provisions into context, and applies senior-partner-level reasoning to cross-reference definitions, spot inconsistencies, and identify patterns.

The Titan's Hybrid Solution

What if you need both speed and depth? The Titan now offers Hybrid Inference: 192GB of DDR5 system RAM enables CPU offloading of the 70B model when you need deep analysis.

Fast Mode (default): 8B model at 200+ tok/sec for drafting and routine work
Deep Think Mode (toggle): 70B model at ~4 tok/sec for complex analysis

It's slower than the Spark's native 70B inference (~45 tok/sec), but it means you can consult the "Senior Partner" brain without buying a second machine. The Titan drafts at the speed of light, but can switch gears to deep analysis when you need it.

Choosing the Right Architecture

Choose The Spark (128GB Unified Memory, 70B Native) for:

M&A due diligence requiring constant deep analysis
Complex litigation document review
Finding contradictions across agreements
Teams of 1-5 users who all need 70B reasoning

Choose The Titan (RTX 5090, 8B + 70B Hybrid) for:

Solo practitioners who need both speed and occasional depth
High-volume contract generation with occasional complex analysis
Firms that want one machine to do both jobs
Budget-conscious buyers who want 70B capability without the Spark's price

Choose The Nomad (RTX 5090 Mobile, 8B Only) for:

Trial lawyers who need AI at depositions and in court
Drafting and summarization on the road
Partners who remote into office Spark/Nexus for deep analysis

The Bottom Line

Don't be fooled by "unlimited context" marketing claims. The real question is: what model can your hardware run?

128GB of unified memory isn't about loading more pages—it's about running smarter models. The Spark's 70B model catches issues that 8B models miss, applies more sophisticated reasoning, and delivers analysis that actually matches partner-level thinking.

For firms handling complex transactions or high-stakes litigation, the intelligence gap between 8B and 70B models isn't just a spec sheet difference—it's the difference between an AI that assists and one that truly advises.