Production-ready GGUF quantization of meta-llama/Llama-3.3-70B-Instruct for distributed text generation and conversation — powered by the Aether edge inference runtime on Edgework.ai.

Model Details

Property	Value
Base model	meta-llama/Llama-3.3-70B-Instruct
Parameters	70B
Architecture	LLaMA
Quantization	Q4_K_M
Format	GGUF
Size	~42 GB
License	llama3.3

Usage

With llama.cpp

./llama-cli -m Llama-3.3-70B-Instruct-Q4_K_M.gguf -p "Your prompt here" -n 256

With Aether (Distributed Inference)

This model is deployed across the Aether distributed inference network. Weights are layer-sharded and distributed across multiple edge nodes for parallel inference.

Also available: `.knot` (sovereign format)

This repo ships llama-3.3-70b-instruct.knot — the model weights in the KNOT container that the Aether distributed-inference runtime loads natively (the GGUF, when present, sits right beside it). A KNOT is a single self-describing file with a JSON table-of-contents, so any single tensor is one HTTP Range request — ideal for streaming weights to edge nodes.

	GGUF	KNOT
Container	format-specific header	single file, JSON table-of-contents
Per-tensor fetch	whole-file oriented	one tensor = one Range request
Ecosystem	broad (llama.cpp, …)	Aether / Gnosis runtime

huggingface-cli download forkjoin-ai/llama-3.3-70b-instruct-gguf llama-3.3-70b-instruct.knot --local-dir ./knots

Full format spec: KNOT_FORMAT.md. Inspect the header with bun run open-source/bitwise/scripts/dump-knot.ts llama-3.3-70b-instruct.knot.

Deployment Architecture

This model runs on the Aether distributed inference runtime — a custom engine that shards model layers across multiple nodes for parallel execution:

Coordinator receives requests and manages token generation
Layer nodes each hold a subset of model layers (6 nodes for this model)
Hidden states flow between nodes via gRPC
Zero cold start via warm pool scheduling

Deployed via Edgework.ai — bringing fast, cheap, and private inference as close to the user as possible.

About

Published by AFFECTIVELY · Managed by @buley

We quantize and publish production-ready models for distributed edge inference via the Aether runtime. Every release is tested for correctness and stability before publication.

All models · GitHub · Edgework.ai

forkjoin-ai/llama-3.3-70b-instruct-gguf

Llama 3.3 70b Instruct (GGUF, Q4_K_M)