Infra2026-06-12 · source-backed

Zyphra's ZAYA1-8B was trained from scratch on AMD Instinct, not NVIDIA.

Issue

2026-06-12

Confidence

source-backed

Sources

Redaction

redacted

Story

Per a roundup, ZAYA1-8B is an Apache-2.0 sparse-MoE with 8B total params and ~760M active per token, trained entirely on AMD hardware. The AMD-only training run is the signal: the open-weight training stack is diversifying off NVIDIA. Verify the numbers against the official model card before relying on them, but the hardware story alone is worth noting.

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Linked by a graph relationship (NVIDIA partners with Google); both cover Apache, MoE; overlapping topics (active, model).

Source trail

roundup

Entities

AMD AMD Instinct Apache MoE NVIDIA Verify ZAYA1 Zyphra

Provenance

Canonical issue: Ramsay Research Agent — June 12, 2026
AI generated: no
Story unit: 2026-06-12-zyphra-s-zaya1-8b-was-trained-from-scratch-on-amd-instinct-not-nvidia
Labels: source-backed, canonical briefing excerpt

Fetching from the wire…

Zyphra's ZAYA1-8B was trained from scratch on AMD Instinct, not NVIDIA.

Story

Related stories

Meta ships Muse Glimmer, a 30B open agent model

Local models became a first-class feature of hosted coding agents in a 48-hour window

AMD may put $5B into Anthropic against a 2-gigawatt MI450 commitment.

NVIDIA Nemotron 3 Nano: Open Agentic Model with RL Training Pipeline

llama.cpp Gets Backend-Agnostic Tensor Parallelism, and the NVIDIA Tax Just Got Optional

DeepSeek V4 Preview Drops: 1.6T Parameters, 1M Context, Apache 2.0, and a 35x Price Gap That Should Make Every Builder Rethink Their Inference Budget

llama.cpp now runs a 295B model on one machine

Gemma 4 Drops Four Open Models, and the 31B Dense Beats a 397B MoE

Source trail

Entities

Provenance

Zyphra's ZAYA1-8B was trained from scratch on AMD Instinct, not NVIDIA.

Story

Related stories

Meta ships Muse Glimmer, a 30B open agent model

Local models became a first-class feature of hosted coding agents in a 48-hour window

AMD may put $5B into Anthropic against a 2-gigawatt MI450 commitment.

NVIDIA Nemotron 3 Nano: Open Agentic Model with RL Training Pipeline

llama.cpp Gets Backend-Agnostic Tensor Parallelism, and the NVIDIA Tax Just Got Optional

DeepSeek V4 Preview Drops: 1.6T Parameters, 1M Context, Apache 2.0, and a 35x Price Gap That Should Make Every Builder Rethink Their Inference Budget

llama.cpp now runs a 295B model on one machine

Gemma 4 Drops Four Open Models, and the 31B Dense Beats a 397B MoE

Source trail

Entities

Provenance