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LatestAmd1.0
AmdReasoning

AMD's Instella-MoE Brings Reasoning to ROCm Hardware

A new open mixture-of-experts model with 16B total parameters and just 3B active is tuned to run on AMD's own accelerator stack.

Jul 23, 2026
NotableOther
Instella-MoE 16B A3B Think

AMD has released Instella-MoE 16B A3B Think, an open-weight reasoning model built as a mixture of experts. The design pairs 16 billion total parameters with roughly 3 billion active at inference, a structure meant to keep compute costs modest while retaining the capacity of a larger network.

The "Think" label signals the model's focus: it is positioned as a reasoning-oriented system rather than a general chat assistant, joining a growing field of open models that expose step-by-step problem solving. It handles text generation alongside its reasoning workload.

Why it matters

The most notable angle here is the hardware. AMD explicitly optimizes Instella-MoE for its ROCm software stack, giving developers on AMD accelerators a first-party option rather than defaulting to models tuned for competing platforms.

  • 16B total parameters, ~3B active per token
  • Reasoning and text generation focus
  • Optimized for AMD's ROCm ecosystem
  • Open weights available on Hugging Face

As an initial 1.0 release, Instella-MoE reflects AMD's continued push to build a credible open-model presence tied to its own silicon. The weights and details are published on Hugging Face under a custom license, so teams weighing it for production should review the terms before deploying.

Sources

  • amd/Instella-MoE-16B-A3B-Think

    Hugging Face

    Visit
OlderKwaipilot Releases KAT-Coder V2.5 Dev, an Agentic MoE CoderKwaipilot · Code · 3 months agoNewerMicrosoft's VibeVoice ASR Goes BitNet for CPU SpeechMicrosoft · Speech → Text · 3 months ago

Get the model

Hugging Face

Specs

Parameters16B · MoE
Active params3B active
Context window33K tokens
Size31.7 GB
PrecisionBF16
ArchitectureInstellaMoEForCausalLM
LicenseOTHER
Downloads896
Likes185

Can you run it?

Runs on a laptop — about 10.7 GB at 4-bit.

  • BF16 (as published)

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    32.9 GB
  • 8-bit

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    18 GB
  • 4-bit

    12 GB GPU (RTX 3060 / 4070) · 16 GB Mac

    10.7 GB
Your machine
  • Mixture-of-experts: all 15.8B parameters must be resident even though only 3B are active per token — active count affects speed, not memory.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMReasoning

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