Ling 3.0 Tiny — Hardware Requirements & GPU Compatibility
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Ling 3.0 Tiny is a 7.9B-parameter open language model from Inclusion AI. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 5.34 GB of VRAM — see which GPUs and Macs can run it below.
25.5K downloads
412 likesSpecifications
- Publisher
- Inclusion AI
- Parameters
- 7.9B
- Architecture
- BailingMoeV3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 157,184
- Release Date
- 2026-08-10
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Ling 3.0 Tiny Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.0 GB | 23.0 GB | 3.35 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.1 GB | 23.1 GB | 3.45 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.5 GB | 23.5 GB | 3.85 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.5 GB | 23.6 GB | 3.95 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.3 GB | 24.4 GB | 4.74 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.2 GB | 25.3 GB | 5.62 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.1 GB | 26.1 GB | 6.51 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.5 GB | 27.5 GB | 7.89 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Ling 3.0 Tiny?
Q4_K_M · 5.3 GBLing 3.0 Tiny (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 131K context window can add up to 19.0 GB, bringing total usage to 24.4 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Ling 3.0 Tiny?
Q4_K_M · 5.3 GB58 devices with unified memory can run Ling 3.0 Tiny, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Ling 3.0 Tiny
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Ling 3.0 Tiny need?
Ling 3.0 Tiny requires 5.3 GB of VRAM at Q4_K_M, or 16.4 GB at BF16. Full 131K context adds up to 19.0 GB (24.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.9B × 4.8 bits ÷ 8 = 4.7 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 19.7 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.3 GBQ4_K_M + full context24.4 GB- What's the best quantization for Ling 3.0 Tiny?
For Ling 3.0 Tiny, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.8 GB.
VRAM requirement by quantization
IQ2_XXS2.8 GBIQ3_S4.0 GBIQ4_XS4.8 GBQ4_K_M ★5.3 GBQ5_05.5 GBBF1616.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Ling 3.0 Tiny on a Mac?
Ling 3.0 Tiny requires at least 2.8 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Ling 3.0 Tiny locally?
Yes — Ling 3.0 Tiny can run locally on consumer hardware. At Q4_K_M quantization it needs 5.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Ling 3.0 Tiny?
At Q4_K_M, Ling 3.0 Tiny can reach ~899 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~123 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.3 × 0.65 = ~974 tok/s
Estimated speed at Q4_K_M (5.3 GB)
~974 tok/s~123 tok/s~974 tok/s~899 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Ling 3.0 Tiny?
At Q4_K_M, the download is about 4.74 GB. The full-precision BF16 version is 15.79 GB. The smallest option (IQ2_XXS) is 2.17 GB.
- Which GPUs can run Ling 3.0 Tiny?
50 consumer GPUs can run Ling 3.0 Tiny at Q4_K_M (5.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Ling 3.0 Tiny?
59 devices with unified memory can run Ling 3.0 Tiny at Q4_K_M (5.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.