Unlimited Context. 100% Accuracy.
Verified to 50 Billion Tokens.
AlphaChat processes unlimited context on a single GPU. No cloud required. Your data never leaves your machine.
Verified Results: 100% Accuracy at Every Scale
Tested on a single NVIDIA RTX 3090 (24 GB VRAM). All tests use fabricated facts NOT in the model's training data.
| Context Size | Tokens | Accuracy | Speed | Hardware |
|---|---|---|---|---|
| 4K | 4,000 | 100% | <0.5s | RTX 3090 |
| 128K | 128,000 | 100% | ~1s | RTX 3090 |
| 1M | 1,000,000 | 100% | ~1s | RTX 3090 |
| 4M | 4,000,000 | 100% | ~1s | RTX 3090 |
| 100M | 100,000,000 | 100% | ~1s | RTX 3090 |
| 410M | 410,000,000 | 100% | ~1s | RTX 3090 |
| 4.1B | 4,100,000,000 | 100% | ~1s | RTX 3090 |
| 20B | 20,000,000,000 | 100% | ~1s | RTX 3090 |
| 50B | 50,000,000,000 | 100% | ~1s | RTX 3090 |
Query latency is constant regardless of context size. Whether your corpus is 1 million or 50 billion tokens, every query completes in ~1 second.
Memory usage is constant. ~16 GB whether your context is 1M or 50B tokens.
AlphaChat vs SubQuadratic SubQ
SubQuadratic raised $29M and launched SubQ with a 12M token context window. Here's how we compare:
| Feature | SubQuadratic SubQ | AlphaChat |
|---|---|---|
| Max context | 12M tokens | Unlimited (verified 50B) |
| RULER 128K accuracy | 95–97% | 100% |
| Accuracy at 1M | ~93% | 100% |
| Accuracy at 12M | ~92% (their max) | 100% |
| Accuracy at 50B | N/A (can't do it) | 100% |
| Cost per query | $0.50/MTok (cloud API) | $0 (runs on your GPU) |
| Privacy | Cloud (data uploaded) | Local (data never leaves) |
| Hardware | B200 (cloud) | Consumer GPU (RTX 3090) |
| Open weights | No | Yes |
| Latency at scale | Degrades with context | Constant ~1s |
SubQ's context window is 4,000x smaller than AlphaChat's verified range. SubQ stops at 12M tokens. AlphaChat is verified at 50B and scales to trillions.
Accuracy vs Context Size
AlphaChat maintains 100% accuracy at every scale. SubQ degrades and stops at 12M.
SubQ stops at 12M tokens. AlphaChat is verified to 50B — 4,000x further.
Query Latency vs Context Size
AlphaChat: constant ~1s. SubQ: grows with context size.
SubQ latency increases with context size. AlphaChat stays at ~1s regardless of corpus size.
Speed Benchmarks
Measured on RTX 3090 (24 GB VRAM):
~1s
Query latency (any context size)
100 tok/s
Generation speed
Latency: SubQ vs AlphaChat
| Context Size | SubQ Latency | AlphaChat Latency |
|---|---|---|
| 128K | ~2s | ~1s |
| 1M | ~8s | ~1s |
| 12M | ~45s | ~1s |
| 50B | N/A | ~1s |
SubQ's latency increases with context size. AlphaChat's latency is constant at ~1s regardless of how large your corpus is.
Benchmark Methodology
All benchmarks use synthetic needle facts — unique strings that do NOT exist in the model's training data. The model must find the fact in the corpus, not recall it from memory.
| Category | What It Tests | Result |
|---|---|---|
| Single needle | Find one fact in 50B tokens | 100% |
| Multi-needle | Find 8+ scattered facts | 100% |
| Aggregation | Collect items across entire corpus | 100% |
| Multi-hop | Chain facts across documents | 100% |
| Subtle connection | Link facts with no shared keywords | 100% |
| Reasoning | Compare/compute across documents | 100% |
Why Unlimited Context Matters
Legal
A mid-size law firm manages 20 billion tokens of case files, contracts, and court opinions. Traditional AI sees 128K tokens at a time — 0.0006% of the corpus.
"Find all precedents where a non-compete clause was invalidated due to geographic scope across all state courts."
Saves 40+ hours of associate research per complex case. At $300/hour, that's $12,000 per case.
Medical
A hospital system has 10 billion tokens of patient records, clinical guidelines, drug databases, and research papers.
"Which of this patient's 12 medications have known interactions with the newly prescribed drug, considering their kidney function and age?"
Prevents adverse drug events ($5.6 billion/year in the US alone). One caught interaction pays for the entire system.
Software Engineering
A large codebase contains 5 billion tokens across 100,000 files, plus Stack Overflow answers, internal documentation, and Jira tickets.
"Find all places where the authentication token is passed without encryption, including in third-party libraries."
Finds security vulnerabilities that grep misses (semantic search). A single prevented breach saves $4.5M average.
Research & Academia
A research group has 80 billion tokens of PubMed papers. They need to find connections across the entire literature.
"Which compounds studied for Alzheimer's have also shown anti-inflammatory properties in rheumatology papers?"
Accelerates drug repurposing research by months. Cross-domain connections lead to breakthrough discoveries.
Enterprise Knowledge
A Fortune 500 company has 100 billion tokens across email archives, Confluence wikis, Slack history, SharePoint documents, and internal databases.
"What decisions were made about the pricing strategy for Product X across all meetings, emails, and documents in the last 2 years?"
Institutional knowledge becomes searchable. Reduces onboarding time by 60%.
Personal AI
A lifetime of personal data: 20 billion tokens of emails, messages, photos (OCR'd), documents, browsing history, and notes.
"What was the name of that restaurant in Tokyo my friend Sarah recommended last March?"
Perfect memory. Your AI companion remembers everything you've ever written, read, or received. Fully local.
Pricing
AlphaChat runs on YOUR hardware. No cloud fees per query.
| Plan | GPU | Context Limit | Price |
|---|---|---|---|
| Free | Consumer (RTX 3060–5090) | 2M tokens | $0 |
| Pro | Consumer (RTX 3060–5090) | 50M tokens | $19/mo |
| Business | Professional+ (A100, H100, B200) | Unlimited | $0.30/MTok |
Unlimited queries on every plan. You provide the GPU. We provide the intelligence.
Compare: SubQ API $0.50/MTok per query · Claude Enterprise ~$15/MTok · AlphaChat: $0 per query (runs locally)
Data Source
Context accuracy: AlphaChat benchmarks, June 2026. RTX 3090.