Complete Guide · Updated June 2026 · NoCloudGPT

ChatGPT Alternatives:
30+ open AI models you can run privately

Every model on this page runs entirely on your hardware — no API keys, no usage logs, no data leaving your machine. Browse the detailed cards below, or visit the full model catalog for 80+ families with per-family deployment guidance.

🚀 Run your first model 📊 Jump to comparison table 🃏 Browse model cards

📢

This page is being expanded. The 13 detailed model cards below cover the original comparison. The full NoCloudGPT catalog now includes 80+ model families — see the complete model catalog or browse Llama, DeepSeek, Qwen, Gemma, and Mistral for the latest flagship families.

📱 The Cloud Route (Easy)

🖥️ The Local Route (Advanced)

Take these three simple steps and deploy any of the AIs below with just a cell phone and a credit card:

>
1. Choose your intelligence. Pick a model from the quick-pick matrix below that fits your exact needs.
>
2. Spin up a cloud GPU. Use a provider like RunPod or Lambda Labs to rent the necessary hardware for pennies an hour.
>
3. Launch and chat. Paste your deployment command and interact with your private AI securely, straight from your phone's browser.

Looking to bypass the cloud entirely?

>
Take full control of your data by running these models natively on your own hardware using Ollama. No monthly subscriptions, no data tracking.

🚀 Deploy this on a home-brew server even faster and pay nothing monthly.

All 13 pages complete. Zephyr added July 2025. Scores reflect LMSYS Chatbot Arena and MT-Bench leaderboards as of publication.

⚡ Quick picks by use case

Not sure where to start? These are the strongest choices for each common scenario. Full details and alternatives are in the comparison table below.

Best overall 7B

OpenChat 3.5

7.81 MT-Bench with C-RLFT training. Runs on 5 GB RAM. Apache 2.0.

Apache 2.0

Best conversation

Starling 7B β

8.09 MT-Bench using RLAIF + Nectar dataset. Top-ranked 7B at release.

Apache 2.0

Best commercial use

Nous Hermes 2

Mixtral-based. Strong reasoning, Apache 2.0, good tool-use support.

Apache 2.0

Complex instructions

WizardLM-2 7B

Evol-Instruct training. 8.01 MT-Bench. 32K context. Apache 2.0.

32K context

Uncensored / custom

Dolphin 2.x

Finetuned to remove refusals. Requires operator-level safety controls.

⚠ Operator responsibility

Speech to text

Whisper large-v3-turbo

99+ languages. MIT licence. 809M params, 6 GB RAM.

MIT · Speech only

Most permissive licence

Zephyr 7B β

MIT licence — no attribution clause. 7.34 MT-Bench. dDPO aligned.

MIT

Intel hardware

Neural Chat 7B

Optimised for Intel CPUs and Gaudi accelerators via OpenVINO.

Intel-optimised

Research / education

GPT-2

MIT licence. 1.5B max. Foundational reference for understanding transformers.

MIT · 124M–1.5B

📊 Full comparison table

Licence key: MIT — most permissive, no restrictions Apache 2.0 — permissive commercial use, attribution required Llama / Community — permissive but with usage thresholds or restrictions Non-commercial — research only, no commercial use Mixed — varies by variant

Showing 13 models

Model	Creator	MT-Bench	Context	Min RAM	Licence	Best for	Guide
Alpaca 7B Stanford · March 2023	Stanford CRFM	N/A*	4K	4–5 GB	Non-commercial	HistoryEducation	Read →
Dolphin 2.x Eric Hartford · 2023–24	Eric Hartford	~7.8	32K	5–6 GB	Apache 2.0	UncensoredCustom agents	Read →
GPT-2 OpenAI · Feb 2019	OpenAI	N/A†	1K	1 GB	MIT	ResearchEducation	Read →
Neural Chat 7B Intel · Nov 2023	Intel Labs	7.98	8K	5–6 GB	Apache 2.0	Intel HWGeneral	Read →
Nous Hermes 2 Nous Research · 2023–24	Nous Research	~8.5	32K	~50 GB	Apache 2.0	CommercialReasoning	Read →
OpenChat 3.5 Tsinghua · Oct 2023	Tsinghua THUNLP	7.81	8K	5–6 GB	Apache 2.0	Best overall 7BChat	Read →
Orca 1 Microsoft · June 2023	Microsoft Research	~7.0	4K	5–6 GB	Non-commercial	ReasoningResearch	Read →
Orca 2 Microsoft · Nov 2023	Microsoft Research	~7.2	4K	5–6 GB	Non-commercial	ReasoningStrategy	Read →
Starling 7B β UC Berkeley · Nov 2023	UC Berkeley	8.09	8K	5–6 GB	Apache 2.0	Best 7B chatRLAIF	Read →
Vicuna 1.5 LMSYS · July 2023	LMSYS Org	7.92	4K	5–6 GB	Non-commercial	ShareGPT styleResearch	Read →
Whisper large-v3-turbo OpenAI · Sept 2022 / Oct 2024	OpenAI	STT only	30s audio	6 GB	MIT	Transcription99+ langs	Read →
WizardLM-2 7B Microsoft · April 2024	Microsoft Research	8.01	32K	5–6 GB	Mixed by variant	Complex tasksCoding	Read →
Zephyr 7B β HuggingFace · Oct 2023	HuggingFace	7.34	8K	5–6 GB	MIT	MIT licenceAlignment study	Read →

* Alpaca predates MT-Bench; contemporary evaluations show it performing below Llama 2-era models. † GPT-2 is a completion model, not a chat assistant — MT-Bench is not applicable. MT-Bench scores from LMSYS Chatbot Arena public leaderboard. RAM figures for Q4_K_M INT4 quantisation via Ollama. Nous Hermes 2 shown as Mixtral 8x7B (47B) variant; a 7B/13B variant exists requiring 5–6 GB RAM.

🃏 Model cards

Each card links to the full guide page. The score shown is MT-Bench unless marked otherwise.

Alpaca 7B

Stanford CRFM · March 2023

Historical

The model that started the open-source instruction-following wave. Fine-tuned Llama on 52K instructions generated for $500 using GPT-3.5 self-instruct. No longer competitive but foundationally important for understanding how the field developed.

4K context 4–5 GB RAM 52K self-instruct

Dolphin 2.x

Eric Hartford · 2023–2024

~7.8

Fine-tuned on the Dolphin dataset to remove alignment guardrails, making it the preferred base for custom agents where the operator implements their own safety layer. Available in Mistral, Mixtral, and Llama 3 variants. Commercial use permitted.

32K context 5–6 GB RAM Uncensored

GPT-2

OpenAI · February 2019

Completion

OpenAI's landmark 2019 model, now fully open under MIT. Not a chat assistant — a text completion model. Indispensable as a teaching reference: small enough to study in full, well-documented, and available in four sizes from 124M to 1.5B.

1K context 1 GB RAM (large) 124M–1.5B

Neural Chat 7B

Intel Labs · November 2023

7.98

Intel's fine-tune of Mistral 7B, optimised for Intel CPU and Gaudi accelerator deployment via OpenVINO. Achieved 7.98 MT-Bench — briefly the highest-scoring 7B model. Ideal when deploying on Intel enterprise hardware.

8K context 5–6 GB RAM Intel OpenVINO

Nous Hermes 2

Nous Research · 2023–2024

~8.5

Nous Research's flagship fine-tune series, available on Mistral 7B, Mixtral 8x7B, and Yi 34B bases. The Mixtral variant is best for commercial deployments requiring strong reasoning and tool use. All variants are Apache 2.0.

32K context 7B / 47B variants Tool use

OpenChat 3.5

Tsinghua THUNLP · Oct 2023

7.81

Uses Conditioned Reinforcement Learning Fine-Tuning (C-RLFT) to distinguish between GPT-4 and GPT-3.5 quality training data. 7.81 MT-Bench matches ChatGPT 3.5 on this benchmark. Strong first choice for most general-purpose local chat.

8K context 5–6 GB RAM C-RLFT

Orca 1

Microsoft Research · June 2023

~7.0

Introduced "explanation tuning" — training on GPT-4's reasoning traces, not just its answers. Demonstrated that small models could learn to reason at much higher quality when given step-by-step explanations during training. Llama 2 base; non-commercial only.

4K context 5–6 GB RAM Explanation tuning

Orca 2

Microsoft Research · Nov 2023

~7.2

Extends Orca's approach by teaching "cautious reasoning" — training models to select appropriate problem-solving strategies (step-by-step, recall, direct answer) rather than defaulting to one approach for all tasks. Llama 2 base; non-commercial only.

4K context 5–6 GB RAM Strategy selection

Starling 7B β

UC Berkeley · November 2023

8.09

UC Berkeley's RLAIF (Reinforcement Learning from AI Feedback) model. Trained PairRM on the Nectar dataset — 183K prompts with GPT-4-ranked completions — then fine-tuned OpenChat 3.5 against it. Highest MT-Bench score at the 7B scale at release.

8K context 5–6 GB RAM RLAIF / PairRM

Vicuna 1.5

LMSYS Org · July 2023

7.92

Fine-tuned on 70K real ShareGPT conversations — making it unusually naturalistic in multi-turn dialogue compared to synthetically trained alternatives. Llama 2 base. Non-commercial only (CC BY NC 4.0). Foundational LMSYS benchmark model.

4K context 5–6 GB RAM ShareGPT data

Whisper large-v3-turbo

OpenAI · Sept 2022 / Oct 2024

STT model

OpenAI's best speech-to-text model, now MIT licensed. large-v3-turbo (809M params, Oct 2024) delivers large-v3 quality at 3× the speed. Supports 99+ languages with automatic language detection. Runs locally in 6 GB RAM.

99+ languages 6 GB RAM 809M params

WizardLM-2 7B

Microsoft Research · April 2024

8.01

Uses Evol-Instruct to automatically generate increasingly complex training data through 5 rounds of LLM-driven evolution. WizardLM-2 7B (Mistral base, Apache 2.0, 32K context) achieves 8.01 MT-Bench. WizardCoder 34B reaches 73.2% HumanEval.

32K context 5–6 GB RAM Evol-Instruct

Zephyr 7B β

HuggingFace · October 2023

7.34

HuggingFace's proof that alignment without human labellers works. Uses Direct Preference Optimisation (dDPO) with the AI-generated UltraFeedback dataset. MIT licence — the most permissive in this guide. Reference model for studying modern alignment techniques.

8K context 5–6 GB RAM dDPO / UltraFeedback

🗂️ Full catalog: 80+ additional private AI families

The model cards above cover the original 13 comparison families in depth. These additional families are covered on their own pages in the NoCloudGPT catalog. Each links to a full page with hardware requirements, licensing, and deployment guidance.

Llama

Meta's flagship. Most deployed open AI.

DeepSeek

Reasoning and coding. MIT licensed.

Qwen

0.5B to 72B. Multilingual. Wide size ladder.

Gemma

Google's open AI. Runs on 2 GB.

Mistral

European AI. Apache 2.0. 128K context.

Phi

Microsoft's small-but-sharp reasoning model.

Llama 4

Vision, 10M context, MoE efficiency.

DeepSeek R1

Shows its reasoning. Auditable chain-of-thought.

Mixtral

47B MoE. Near-70B quality, less compute.

Command R

Cohere RAG. Answers from your documents.

Granite

IBM-backed enterprise AI. Certified training data.

LLaVA

Open vision AI. Analyze images privately.

Falcon

TII. Arabic-first, permissive commercial terms.

Aya

Cohere. 100+ languages, not just English.

Cogito

Reasoning and agentic tool use.

DeepSeek Coder

Private Copilot alternative. NDA-safe.

Qwen Coder

40+ languages. 1.5B to 72B.

OLMo

Fully open: weights, data, checkpoints.

Phi-4

14B, MIT license, no GPU required.

+ 60 more families →

⚖️ How these compare to proprietary models

🤖

vs ChatGPT (GPT-4o)

Privacy, cost, benchmark comparison. When local models match GPT-4 and when they don't.

🧠

vs Claude 3.5 Sonnet

Long-context performance, safety approach, and which local models come closest.

💻

vs GitHub Copilot

Code completion, IDE integration, and cost analysis for local coding alternatives.

🚀 Getting started

All models on this page can be running locally in under five minutes using Ollama. The quickstart covers installation on macOS, Linux, and Windows, with hardware recommendations matched to each model.

bash — try any model in one command

# Install Ollama: https://ollama.com
# Then pick any model from this page:

ollama run openchat    # Best overall 7B — Apache 2.0
ollama run starling    # Best 7B conversation — Apache 2.0
ollama run zephyr      # Most permissive licence — MIT
ollama run wizardlm2   # Complex instructions — Apache 2.0
ollama run dolphin     # Uncensored custom agents
ollama run whisper     # Speech-to-text — MIT

🚀

Quickstart: Ollama setup

macOS, Linux, Windows — with hardware requirements per model size.

⚖️

vs ChatGPT: full comparison

Privacy risks, benchmark gaps, and recommended local alternatives by use case.

🧠

vs Claude: full comparison

Long-context analysis, Constitutional AI vs open alignment methods.

💻

vs Copilot: full comparison

Code-focused evaluation, IDE plugins, and WizardCoder vs Copilot benchmarks.

✓

Every model on this page runs without an internet connection

After the initial download, all inference is local. No prompts are sent to external servers. No usage data is collected. Your conversations stay on your machine. This guide exists because that privacy guarantee matters — and because the quality of open-source models now makes it practical.