Ling 2.6 1T
By Ant Group / inclusionAI · China
Updated 2026-07-13
Overview
Ant Group's Ling 2.6 1T: MIT-licensed MoE with 50B active params, hybrid MLA + Linear Attention, and 256k context. Top open non-reasoning model with an Intelligence Index of 34.
When to pick this model
- Agentic workloads needing mature tool calling at frontier scale
- Long-context analysis up to 256k tokens
- MIT-licensed datacenter deployments
- Non-reasoning workloads where speed beats deliberation
- Replacing closed flagships with open weights
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 580 GB |
| Q5_K_M | 710 GB |
| Q8_0 | 1070 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, Ling 2.6 1T is server-class even at Q4_K_M (580 GB). Stepping up to Q8_0 nearly doubles the footprint to 1070 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, Ling 2.6 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 1 tokens/sec on entry-level GPUs, on the order of 4 tokens/sec on a mid-range card, and up to 12 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Ling 2.6 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 Ling 2.6 1T |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 580 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 580 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 580 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 580 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 580 GB at Q4_K_M |
Which GPU should you buy to run Ling 2.6 1T?
To run Ling 2.6 1T locally at Q4, you need ~580 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| AA Intelligence Index | 34 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- Permissive MIT license
- Top open non-reasoning Intelligence Index (34)
- 256k context window
- Efficient hybrid MLA + Linear Attention
- Mature agentic tool calling, compatible with Qwen2.5 parsers
Limitations
- Around 600 GB VRAM in Q4 — datacenter required
- Hugging Face weights only — no Ollama tag
- Not a reasoning model; pick DeepSeek V4 for deliberation
Typical workloads
In our catalog grid, Ling 2.6 1T is filed under Open Frontier Chat, Long Context, Tool-Calling Agents — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
The 256k-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: BailingMoeV2.5 · MoE 1T total / 50B active · 256 experts top-8 + 1 shared · 80 layers · hybrid MLA + Linear Attention · 256k ctx
Training: Ling 2.6 family (Ant Group). Contextual Process Redundancy Suppression and 'Fast Thinking' strategy to reduce token overhead. Qwen2.5-compatible tool-call parser.
The MIT-licensed flagship to beat for non-reasoning, agentic workloads at trillion-parameter scale.
Quick start
# HuggingFace : inclusionAI/Ling-2.6-1TOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Ling 2.6 1T need?
At the recommended Q4_K_M quantization, Ling 2.6 1T needs about 580 GB of VRAM. Q8_0 takes 1070 GB, and unquantized FP16 weights take 2000 GB.
Can Ling 2.6 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 Ling 2.6 1T support?
Ling 2.6 1T supports a 256k-token context window (262,144 tokens).
Can I use Ling 2.6 1T commercially?
Yes. Ling 2.6 1T is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Ling 2.6 1T on consumer hardware?
Our compatibility engine estimates on the order of 4 tokens/sec on a mid-range GPU and up to 12 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Ling 2.6 1T should I download first?
Start with Q4_K_M (580 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.