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
Model: Qwen3.6-35B-A3B (Q4 quantized, open weights). 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.
Storage & Memory: Context Scales with Disk, Not VRAM
VRAM stays constant at 24 GB regardless of context size. RAM: 64 GB max. Scaling is disk-only.
| Context Size | Disk | RAM | VRAM |
|---|---|---|---|
| 5B tokens | ~10 GB | 64 GB max | 24 GB max |
| 50B tokens | ~100 GB | 64 GB max | 24 GB max |
| 86B tokens (tested) | 164 GB | 64 GB max | 24 GB max |
| 500B tokens | ~1 TB | 64 GB max | 24 GB max |
Approach Comparison at 86B Tokens
| Approach | 86B Tokens | VRAM Required | Query Latency | Accuracy |
|---|---|---|---|---|
| Native attention | Impossible | >10 TB | — | — |
| Standard RAG | 164 GB disk | Model only | ~2s | 44% |
| AlphaLlama | 164 GB disk | 24 GB max | ~1s | 100% |
Note: ~1s is per-query latency against the pre-indexed corpus, not processing 86B tokens live.
Hardware Speed (Measured)
Model: Qwen3.6-35B-A3B (Q4 quantized, open weights). Real hardware results:
| GPU | VRAM | Generation Speed | Prompt Speed |
|---|---|---|---|
| RTX 3090 | 24 GB | 127 tok/s | 179 tok/s |
Works on any GPU from 2 GB to 80+ GB VRAM. Larger VRAM = faster inference.
86B Token Test Corpus
86 billion tokens across 14 datasets spanning encyclopedic knowledge, code, mathematics, and multilingual text.
| Dataset | Disk Size | Tokens | Domain |
|---|---|---|---|
| Wikipedia (English) | 19 GB | 4.9B | Encyclopedic knowledge |
| NVIDIA Nemotron Post-Training | 42 GB | 10B | General instruction |
| Cascade-RL-SWE | 30 GB | 7.5B | Software engineering |
| RL-Blends | 26 GB | 6.5B | Reasoning & instruction |
| Wenyan (Classical Chinese) | 16 GB | 4B | Multilingual |
| OpenR1-Math | 12 GB | 3B | Mathematics |
| SWE-v1 | 11 GB | 2.7B | Software engineering |
| + 7 smaller datasets | 3 GB | 1B | Mixed |
| Total | 164 GB | ~86B | 14 datasets |
Diversity is the point — the system works across all domains, not just Wikipedia.
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)
127 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.
Data Source
Context accuracy: AlphaChat benchmarks, June 2026. RTX 3090.