Ring-1T

Overview

Ant Group's MIT-licensed open reasoner: 1T total parameters with 50B active, using a novel ring-all-reduce MoE architecture. Top of the open-reasoning leaderboards.

When to pick this model

• Datacenter-scale reasoning workloads
• Research into novel MoE architectures
• Frontier benchmarking against closed reasoners
• Long-context reasoning up to 131k tokens

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	600 GB
Q5_K_M	720 GB
Q8_0	1080 GB
FP16 (no quantization)	2000 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Ring-1T is server-class even at Q4_K_M (600 GB). Stepping up to Q8_0 nearly doubles the footprint to 1080 GB, and unquantized FP16 weights take 2000 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Ring-1T needs roughly 700 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 0.5 tokens/sec on entry-level GPUs, on the order of 3 tokens/sec on a mid-range card, and up to 10 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Ring-1T to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

GPU memory	Example cards	Best fit for Ring-1T
8 GB	RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB	Does not fit — needs 600 GB at Q4_K_M
12 GB	RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop	Does not fit — needs 600 GB at Q4_K_M
16 GB	RTX 5080, RTX 4080 Super, Radeon RX 9070 XT	Does not fit — needs 600 GB at Q4_K_M
24 GB	RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop	Does not fit — needs 600 GB at Q4_K_M
32 GB	RTX 5090	Does not fit — needs 600 GB at Q4_K_M

Which GPU should you buy to run Ring-1T?

To run Ring-1T locally at Q4, you need ~600 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).

Strengths

• First trillion-parameter Chinese open-weight model
• MIT license with full commercial freedom
• Original ring-MoE all-reduce architecture
• 131k context window

Limitations

• Around 600 GB VRAM at Q4 — datacenter only
• Commercial licensing for downstream use is complex
• Operationally heavy to deploy and tune

Typical workloads

In our catalog grid, Ring-1T is filed under Frontier Reasoning, Advanced Math — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.

The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: MoE ring-all-reduce · 1T total · Ant Group · 131k context

Training: Ant Group — first Chinese trillion-parameter open-weight model, ring-MoE architecture.

Quick start

# Infrastructure data-center requise — non disponible en local standard

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

Similar models worth comparing

Frequently asked questions

How much VRAM does Ring-1T need?

At the recommended Q4_K_M quantization, Ring-1T needs about 600 GB of VRAM. Q8_0 takes 1080 GB, and unquantized FP16 weights take 2000 GB.

Can Ring-1T run without a GPU?

Yes — with roughly 700 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Ring-1T support?

Ring-1T supports a 128k-token context window (131,072 tokens).

Can I use Ring-1T commercially?

Yes. Ring-1T is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is Ring-1T on consumer hardware?

Our compatibility engine estimates on the order of 3 tokens/sec on a mid-range GPU and up to 10 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Ring-1T should I download first?

Start with Q4_K_M (600 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.