PowerMoE-3b
ibm-researchapache-2.0text

PowerMoE-3b

Updated Jun 7, 2026
Parameters
3.4B
Context
4,096
License
apache-2.0
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 PowerMoE-3b with real runtime overhead.
Q4_K_M
2 GB
Q5_K_M
2 GB
Q8_0
4 GB
FP16
7 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 ibm-research/PowerMoE-3b

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

Deep dive

Notes, sources, and the full write-up

PowerMoE-3b

PowerMoE-3b is a 3.4B-parameter apache-2.0 model from ibm-research. At Q4_K_M it needs roughly 2 GB of VRAM, placing it in the cpu hardware tier.

Specifications

SpecValue
Parameters3.4B
Context length4K tokens
Licenseapache-2.0
Modalitiestext
Released2024-08-14
Weightsibm-research/PowerMoE-3b

VRAM requirements

QuantVRAMRuns on
Q4_K_M~2 GBRTX 4060, RTX 3060 8GB
Q5_K_M~2 GBRTX 4060, RTX 3060 8GB
Q8_0~4 GBRTX 4060, RTX 3060 8GB
FP16~7 GBRTX 4060, RTX 3060 8GB

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

How to run

vLLM:

vllm serve ibm-research/PowerMoE-3b

Popularity

PowerMoE-3b has 1,621,298 downloads in the last month on HuggingFace and 20 likes.

Frequently asked

Quick answers to common questions

How much VRAM does PowerMoE-3b need?

PowerMoE-3b with 3.4B parameters needs approximately 2 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is PowerMoE-3b better than other ibm-research models?

PowerMoE-3b has 3.4B parameters with 4,096 context - a strong choice for general use.

What license is PowerMoE-3b under?

PowerMoE-3b is released under the apache-2.0 license, making it suitable for most commercial and personal projects.

What hardware runs PowerMoE-3b well?

With 3.4B parameters, PowerMoE-3b 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 PowerMoE-3b?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~2 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

What models compete with PowerMoE-3b?

PowerMoE-3b 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.