# Edge0-145B: Open 145B Agent Model Built to Run on Your Laptop

> Source: <https://explainx.ai/blog/edge0-145b-open-on-device-laptop-agent-model-november-2026?utm_source=rss&utm_medium=feed&utm_campaign=blog>
> Published: 2026-11-13 00:00:00+00:00

**Edge0 open-sourced Edge0-145B and Edge0-120B on November 12, 2026**, calling Edge0-145B the first open **145-billion-parameter** model aimed at **personal-computer agents** rather than datacenter-only serving. The [announcement](https://edge0.ai/blog/introducing-edge0-145b-frontier-ai-capabilities-on-your-own-computer?ref=explainx) pairs weights on [Hugging Face](https://huggingface.co/Edge0/Edge0-145B-q3k?ref=explainx) with **edge0-infer** and **edge0-strata** on [GitHub](https://github.com/Edge0-AI/Edge0?ref=explainx), continuing a line that began with the [35B phone-scale release](https://explainx.ai/blog/edge0-35b-on-device-model-apple-silicon-2026) in September.

If you benchmark agents locally, this is a **different design bet** from [InferSSD's SSD-streamed 800B path](https://explainx.ai/blog/inferssd-800b-consumer-hardware-llama-cpp-november-2026) or [Apple's four-Mac 800B RDMA demo](https://explainx.ai/blog/apple-mlx-rdma-thunderbolt-5-mac-studio-m5-ultra-muse-spark-open-local-inference-2026): Edge0 trains a **latent sparse MoE** for **one NVIDIA GPU** with **predictive expert loading**, not full-weight RAM residency.

## TL;DR

| Question | Answer (Nov 12–13, 2026) | 
|---|---|
| Models | **Edge0-145B** and**Edge0-120B** open weights | 
| License | **Apache 2.0** stack on GitHub per repository header | 
| Checkpoints | **Edge0/Edge0-145B-q3k** ,**Edge0/Edge0-145B-NVFP4** , plus 120B variants | 
| Engine | **edge0-strata** plus**edge0-infer serve/chat** | 
| GPU requirement | **CUDA 13** , compute capability**7.5+** ,**16GB+ VRAM** per GitHub v0.2.0 notes | 
| Claimed memory | **29GB peak** on compatible laptop in announcement | 
| Claimed decode | **80+ tok/s** decode,**1100+ tok/s** prefill in announcement | 
| Benchmark caveat | Most table rows are **Edge0 internal evals** | 

## What shipped in the stack

Edge0's November release is **three artifacts**, not a weights-only drop:

1. **Model folders** on Hugging Face with`model.json` metadata for edge0-infer.
2. **edge0-strata** , a Strata fork that implements latent experts, SSD-tier vocabulary, and speculative paths described in the announcement.
3. **edge0-infer v0.2.0** , packaging CLI**`serve`** ,**` chat`** , OpenAI-compatible local server, and desktop**Edge0 Chat** without bundling weights.

GitHub release notes dated **November 11, 2026** (`v0.2.0`) state Windows x64 and arm64 packages, **Anthropic Messages API** on the local server, and explicit separation: **145B does not ship inside the zip**; you download checkpoints separately.

That matches how serious local stacks should ship: **engine first**, **multi-hundred-gigabyte folders second**.

## Architecture: why 145B can claim laptop memory

Edge0 chart of largest open on-device models by lab through November 2026
*Image: [Edge0](https://edge0.ai/blog/introducing-edge0-145b-frontier-ai-capabilities-on-your-own-computer?ref=explainx)*

Edge0's post breaks the design into **four layers** — architecture, training, loading, scheduling — we summarize with their vocabulary:

**Shared expert matrices.** Groups of **64 experts** share large matrices; only about **10B of 145B parameters** activate per step in the announcement narrative.

**Training for local execution.** Models are trained in the **on-device architecture**, not compressed after a cloud training run — a distinction we also stressed for [Janas disk-streaming MoE](https://explainx.ai/blog/janas-run-moe-llms-larger-than-ram-stream-experts-ssd-october-2026).

**Three-tier loading.** Hot experts in GPU memory, a **~40GB expert library** in system RAM, **~48GB vocabulary data** on SSD with on-demand reads.

**CPU/GPU co-scheduling** plus **speculative decoding** to hit the cited **80+ tok/s** decode.

The approach rhymes with **WiCi InferSSD** expert streaming but targets **145B total** rather than **800B-class** checkpoints — different scale, similar **memory-wall** philosophy.

### Coding and reasoning that rival frontier cloud models

Edge0-145B is competitive with specific frontier cloud models across coding, reasoning, knowledge, and instruction following:

The announcement table highlights:

- **DeepSWE 1.1: 64.6** versus**Qwen3.8-Flash-Next 58.7**
- **Terminal-Bench 2.1: 86.52** versus**Claude-Opus-4.6 (Max) 78.2** in Edge0's table
- **GPQA-Diamond: 90.4** near**DeepSeek-V4-Flash-0731 90.8**

Read the footnotes: **Claude lacks a public DeepSWE score** in their table, and **Flint/Muse/Gemma** rows mix **Recursive Flint** and **Artificial Analysis** sources. This is useful for **directional** comparisons, not leaderboard certification.

Edge0 also claims **72.8 mean** across six benchmarks at **29GB measured peak**, versus **~70GB estimated** for Qwen3.8-Flash-Next in 4-bit deployment — enabling **48GB laptops** in theory, if your GPU partition matches their test rig.

Edge0 benchmark mean score versus peak memory for laptop-class models
*Image: [Edge0](https://edge0.ai/blog/introducing-edge0-145b-frontier-ai-capabilities-on-your-own-computer?ref=explainx)*

## Hugging Face card: parameters you can verify

The [Edge0-145B-q3k model card](https://huggingface.co/Edge0/Edge0-145B-q3k?ref=explainx) lists mechanical facts independent of marketing copy:

| Field | Value on card | 
|---|---|
| Total parameters | **145B** ,**10.12B active per token** | 
| Layers | **48** hybrid attention | 
| Experts | **512** per layer,**10 routed + 1 shared** | 
| Context | **256K** , KV int8 with**32K resident** | 
| Q3_K size | **40.81 GiB** on disk | 
| NVFP4 size | **47.79 GiB** | 

Throughput on card: **121.2 tok/s agent decode** and **3554.9 tok/s 16K prefill** on **RTX 5090 D 24GB** — faster than the blog's generic **80+ tok/s** line because the card names a specific **agent decode** profile.

Builders should treat **test RAM budgets** (16GB VRAM + 20GB host) as **engineering limits from one profile**, not a universal minimum.

## How to run Edge0-145B today

From the Hugging Face quick start (paraphrased with paths intact):

```
git clone https://github.com/Edge0-AI/Edge0.git edge0
```

Build **edge0-strata** using `EDGE0.md` for Linux or Windows NVIDIA, then **edge0-infer** per `edge0-infer/docs/getting-started.md`. Download **` Edge0-145B-q3k`** or **` Edge0-145B-NVFP4`** into its own folder.

```
build/cpu/bin/edge0-infer serve --model /path/to/Edge0-145B-q3k
```

Pass the **leaf model folder**, not the parent directory holding multiple checkpoints. On Windows, GitHub points to `build\windows\bin\edge0-infer.exe`.

**OpenAI-compatible** local server plus **Messages API** support (per v0.2.0 notes) means you can point harnesses that expect cloud APIs at **localhost**, similar to patterns in [GPU task managers trending this week](https://explainx.ai/blog/shanselman-gputaskman-gpu-llm-task-manager-2026) but with **MoE-scale** weights.

## Platform matrix: who can run what

| Platform | Edge0-145B / 120B | Edge0-35B | 
|---|---|---|
| Linux NVIDIA | **edge0-strata** | CPU native engine | 
| Windows NVIDIA | **edge0-strata** | CPU or Vulkan | 
| macOS Apple Silicon | **Not in strata table** | **Metal** | 

If your team is **Mac-only**, this release is **watchlist**, not **production** — unless you remote into a Windows/Linux box. For Mac local agents, compare [TensorFold Zig inference](https://explainx.ai/blog/tensorfold-zig-local-llm-inference-metal-cuda-api-2026) and the existing **35B** path.

## What developers are saying on Hacker News

A small thread (`~21` points) asked practical questions rather than celebrating star counts:

- **mortalapeman** asked whether 145B is**derived from Qwen3.8-Flash-Next** , noting strong hands-on results with Strata on that family — consistent with GitHub's derivation line.[Comment](https://news.ycombinator.com/item?id=50476512&ref=explainx)
- **fesoliveira** said third-party benchmarking would determine excitement, flagging healthy skepticism at the parameter count.[Comment](https://news.ycombinator.com/item?id=50476588&ref=explainx)
- **Zetaphor** corrected terminology:**open weights** , not**open source** for the whole stack — fair when chat apps remain partially packaged.[Comment](https://news.ycombinator.com/item?id=50476575&ref=explainx)
- **SwellJoe** praised**Blender tool-use demos** but doubted whether outputs are human-refinable — the same maturity question we raised for[agentic video pipelines](https://explainx.ai/blog/vimax-agentic-video-generation-complete-guide-2026) .[Comment](https://news.ycombinator.com/item?id=50476687&ref=explainx)

No independent Terminal-Bench reproduction appeared in the first dozen comments we fetched.

## Agent workflows the announcement emphasizes

Edge0's public demos lean on **tool loops** — Blender scenes, PCB edits, small games — not single-shot chat. That matters for harness design:

- **Plan → code → execute → visually verify** matches how[Expo EAS simulator agents](https://explainx.ai/blog/expo-simulators-eas-simulator-ai-agent-cloud-ios-simulator-verify-change-recordings-guide-2026) record UI work, but Edge0 claims the loop stays**on-machine** .
- **128K–256K context** in the announcement supports**multi-file repo edits** without re-uploading zip archives to a cloud API.
- **Offline core** in the Edge0 App removes quota anxiety for travelers, similar to why teams experiment with[Hugging Face Trellis 3D LoRA](https://explainx.ai/blog/huggingface-3d-lora-finetuning-trellis-guide-2026) locally before paying hosted GPUs.

If your product only needs **summarization**, 145B local is probably overpowered. If you run **SWE-bench style** tasks, this release is aimed at you.

## Licensing, derivation, and compliance

GitHub marks the **Edge0 repository** under **Apache 2.0**. Weights ship as separate folders on Hugging Face with their own cards. Because models are **derived from Qwen3.8-Flash-Next**, read **upstream license stacking** before redistributing merged checkpoints in a commercial appliance.

Export-control news — [Tim Scott's Banking testimony requests](https://explainx.ai/blog/tim-scott-senate-banking-huang-lutnick-testimony-november-2026) and [Singapore's Megaspeed probe](https://explainx.ai/blog/nvidia-jensen-huang-chinese-spy-plot-megaspeed-alice-huang-trump-briefed-claimed-vs-verified-2026) — does not change open-weight downloadability today, but it can change **who may host** large MoE endpoints in regulated industries.

## Honest limitations

**Vendor benchmarks.** Until Artificial Analysis or community harnesses publish runs, treat **Terminal-Bench 86.52** as **Edge0-reported**.

**Hardware lock-in for 145B.** CUDA 13 plus recent NVIDIA GPUs excludes **AMD**, **Intel Arc**, and **Apple** for this tier today.

**Disk and thermals.** SSD vocabulary streaming and **120W typical power** in the announcement mean **laptop cooling profiles** matter as much as VRAM spreadsheets.

**Derivation lineage.** Starting from **Qwen3.8-Flash-Next** may affect **license stacking** and **fine-tune compatibility** — read both Edge0 and upstream cards before merging adapters.

**Not a cloud replacement for every task.** Edge0's own closing section keeps **cloud** for **very low-compute devices** and **fresh web search** — same split we document for [Bedrock agent ROI](https://explainx.ai/blog/aws-bedrock-cost-per-task-not-per-token-agent-roi-baselines-holdouts-part-2-2026).

## How Edge0-145B compares to neighbor releases

| Approach | Scale | Mechanism | explainx.ai pointer | 
|---|---|---|---|
| Edge0-145B | 145B MoE | Latent experts + strata GPU | This post | 
| Edge0-35B | 35B MoE | Phone/laptop preview | [35B guide](https://explainx.ai/blog/edge0-35b-on-device-model-apple-silicon-2026) | 
| InferSSD | 763B–816B | SSD expert reads + llama.cpp | [InferSSD post](https://explainx.ai/blog/inferssd-800b-consumer-hardware-llama-cpp-november-2026) | 
| Janas | Varies | Disk streaming engine | [Janas MoE](https://explainx.ai/blog/janas-run-moe-llms-larger-than-ram-stream-experts-ssd-october-2026) | 
| Inkling 2 Small | 594B MoE | Datacenter NVFP4 | [Inkling weights](https://explainx.ai/blog/inkling-2-small-thinking-machines-594b-closed-weights-apache-next-week-2026) | 

Pick **145B Edge0** when you want **single-GPU agent loops** with **sub-50GB RAM stories**. Pick **InferSSD** when you already downloaded **DeepSeek-V4.1-Flash** or **Muse-Spark-Open** GGUFs and accept **2–7 tok/s decode**.

## Repro checklist before you file a bug

Edge0's stack is new enough that **misconfigured paths** dominate early issue threads:

1. Confirm **`model.json` `model_name`** matches the folder you pass to` serve` .
2. Install **CUDA Toolkit 13** on Windows if`edge0-strata` fails device init — GitHub release notes are explicit.
3. Download **NVFP4** only if you have spare disk; Q3_K is the documented default for the 5090 profile.
4. Run **`edge0-infer info`** (if available in your build) before blaming model quality.
5. Compare against **cloud baselines** with the same prompt file — apples-to-oranges prompts made[Podqi shopping safety stats](https://explainx.ai/blog/podqi-ai-shopping-answers-risky-sites-9-percent-perplexity-chatgpt-gemini-claimed-vs-verified-2026) look worse until methodology aligned.

## Related reading

*Weights paths, CUDA requirements, and benchmark tables reflect Edge0's announcement, Hugging Face model card, and GitHub v0.2.0 release notes as of November 13, 2026.*