LLaDA-8B-Instruct
GSAI-MLmittext

LLaDA-8B-Instruct

Updated Jun 7, 2026
Parameters
8B
Context
8,192
License
mit
Updated
Jun 7, 2026

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at FP16
4 of 4 quantizations fit LLaDA-8B-Instruct with real runtime overhead.
Q4_K_M
5 GB
Q5_K_M
6 GB
Q8_0
9 GB
FP16
16 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve GSAI-ML/LLaDA-8B-Instruct

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

LLaDA-8B-Instruct

LLaDA-8B-Instruct is a 8B-parameter mit model from GSAI-ML. At Q4_K_M it needs roughly 5 GB of VRAM, placing it in the 8–12 GB GPU hardware tier.

Specifications

SpecValue
Parameters8B
Licensemit
Modalitiestext
Released2025-02-19
WeightsGSAI-ML/LLaDA-8B-Instruct

VRAM requirements

QuantVRAMRuns on
Q4_K_M~5 GBRTX 4060, RTX 3060 8GB
Q5_K_M~6 GBRTX 4060, RTX 3060 8GB
Q8_0~9 GBRTX 3060 12GB, RTX 4070
FP16~16 GBRTX 4060 Ti 16GB, RTX 4080

VRAM is estimated from parameter count; MoE models still need all weights resident.

How to run

vLLM:

vllm serve GSAI-ML/LLaDA-8B-Instruct

Popularity

LLaDA-8B-Instruct has 793,372 downloads in the last month on HuggingFace and 358 likes.

Frequently asked

Quick answers to common questions

How much VRAM does LLaDA-8B-Instruct need?

LLaDA-8B-Instruct with 8B parameters needs approximately 5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is LLaDA-8B-Instruct better than other GSAI-ML models?

LLaDA-8B-Instruct has 8B parameters with 8,192 context - a strong choice for general use.

What license is LLaDA-8B-Instruct under?

LLaDA-8B-Instruct is released under the mit license, making it suitable for most commercial and personal projects.

What hardware runs LLaDA-8B-Instruct well?

With 8B parameters, LLaDA-8B-Instruct requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for LLaDA-8B-Instruct?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~6 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

What models compete with LLaDA-8B-Instruct?

LLaDA-8B-Instruct competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec

Comments coming soon

Configure NEXT_PUBLIC_GISCUS_REPO_ID and NEXT_PUBLIC_GISCUS_CATEGORY_ID at giscus.app to enable.