FitMyLLM
Ant Group/Mixture of Experts

ALing-lite 16.8B

Ling is a MoE LLM provided and open-sourced by InclusionAI. We introduce two different sizes, which are Ling-Lite and Ling-Plus.

chat
16.8B
Parameters (2.4B active)
32K
Context length
0
Benchmarks
10
Quantizations
1K
HF downloads
Architecture
MoE
Released
2025-02-28
Layers
28
KV Heads
4
Head Dim
128
Family
other

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
9.5 GB
8.9 + 0.7 KV
low
Q3_K_L4.3
10.2 GB
9.5 + 0.7 KV
moderate
IQ4_XS4.46
10.5 GB
9.9 + 0.7 KV
moderate
Q4_K_S4.67
11.0 GB
10.3 + 0.7 KV
moderate
Q4_K_M4.89
11.4 GB
10.8 + 0.7 KV
good
Q5_K_S5.57
12.8 GB
12.2 + 0.7 KV
good
Q5_K_M5.7
13.1 GB
12.5 + 0.7 KV
good
Q6_K6.56
14.9 GB
14.3 + 0.7 KV
excellent
Q8_08.5
19.0 GB
18.3 + 0.7 KV
lossless
FP1616
34.7 GB
34.1 + 0.7 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN LING-LITE 16.8B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run other:17b-q4_K_M

Tag may need adjustment — check ollama.com/library/other for available tags.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA Tesla K40c
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40d
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40m
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40s
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40st
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40t
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K80
12 GB VRAM • 241 GB/s
NVIDIA
NVIDIA Tesla M40
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P100 PCIe 12 GB
12 GB VRAM • 549 GB/s
NVIDIA
NVIDIA RTX A2000 12 GB
12 GB VRAM • 288 GB/s
NVIDIA
$550

Find the best GPU for Ling-lite 16.8B

Build Hardware for Ling-lite 16.8B
▸ SPEC SHEET

Ling-lite 16.8B16.8B MoE.

▸ SPECIFICATIONS
PARAMETERS
16.8B (2.4B active)
ARCHITECTURE
Mixture of Experts
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat
RELEASE DATE
2025-02-28
PROVIDER
Ant Group
FAMILY
other
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M48.9 GB88%
Q3_K_L4.39.5 GB90%
IQ4_XS4.469.9 GB92%
Q4_K_S4.6710.3 GB93%
Q4_K_M4.8910.8 GB94%
Q5_K_S5.5712.2 GB96%
Q5_K_M5.712.5 GB96%
Q6_K6.5614.3 GB97%
Q8_08.518.3 GB100%
FP161634.1 GB100%
§ 02RUN COMMAND

Run Ling-lite 16.8B locally with Ollama — needs 10.8 GB VRAM at Q4_K_M:

$ollama run other:16b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M