dots.llm1 Instruct
By Rednote · China
Updated 2026-07-13
Overview
Xiaohongshu's first LLM under the Rednote brand — a 142B MoE with 14B active params trained without synthetic data, matching Qwen2.5-72B. Released under MIT.
When to pick this model
- Creative and lifestyle content generation
- Chinese-language social and consumer-facing products
- Research on training without synthetic data
- MIT-licensed alternative to Qwen for content-heavy use cases
- Workloads where natural, non-generic prose matters
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 85 GB |
| Q5_K_M | 102 GB |
| Q8_0 | 152 GB |
| FP16 (no quantization) | 284 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, dots.llm1 Instruct is server-class even at Q4_K_M (85 GB). Stepping up to Q8_0 nearly doubles the footprint to 152 GB, and unquantized FP16 weights take 284 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, dots.llm1 Instruct needs roughly 120 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 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 30 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches dots.llm1 Instruct 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 dots.llm1 Instruct |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 85 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 85 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 85 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 85 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 85 GB at Q4_K_M |
Which GPU should you buy to run dots.llm1 Instruct?
To run dots.llm1 Instruct locally at Q4, you need ~85 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Strengths
- 14B active params in a 142B MoE — efficient inference
- MIT license
- Strong creative and lifestyle content generation
- No synthetic data in training — more natural outputs
Limitations
- Roughly 85 GB VRAM in Q4 — multi-GPU territory
- 32k context lags modern flagships
- Output style optimized for Chinese social media — may not fit Western tone
Typical workloads
In our catalog grid, dots.llm1 Instruct is filed under Data Transparency, Research — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.
The 32k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE · 142B total / 14B active · Rednote (Xiaohongshu) · 32k ctx
Training: Rednote — strong in creative generation and lifestyle content.
An MIT-licensed alternative for creative Chinese content; outside that niche, Qwen3 is the safer pick.
Quick start
ollama pull hf.co/rednote/dots-llm1-GGUFOr 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 dots.llm1 Instruct need?
At the recommended Q4_K_M quantization, dots.llm1 Instruct needs about 85 GB of VRAM. Q8_0 takes 152 GB, and unquantized FP16 weights take 284 GB.
Can dots.llm1 Instruct run without a GPU?
Yes — with roughly 120 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 dots.llm1 Instruct support?
dots.llm1 Instruct supports a 32k-token context window (32,768 tokens).
Can I use dots.llm1 Instruct commercially?
Yes. dots.llm1 Instruct is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is dots.llm1 Instruct on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 30 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of dots.llm1 Instruct should I download first?
Start with Q4_K_M (85 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.