{"slug": "can-i-run-a-gpt-5-codex-review-locally-using-ollama", "title": "Can I run a GPT-5 Codex review locally using Ollama?", "summary": "A developer tested running coding models locally via Ollama and found that DeepSeek-Coder-V2 on an RTX 3090 delivers 0.2s time to first token and 45 tokens per second, compared to GPT-4o's 1.1s and 60 t/s, with full privacy and zero cost. The article explains that OpenAI does not release weights for GPT-5 Codex, so open-weights models like DeepSeek-Coder-V2 or CodeLlama are used instead, and recommends integrating them with Continue.dev and MCP for a professional workflow.", "body_md": "# Can I run a GPT-5 Codex review locally using Ollama?\n\n[DeepSeek](/en/tags/deepseek/)-Coder-V2 or CodeLlama via Ollama to get a similar experience.\n\nThe mechanism is simple: Ollama acts as a local inference engine that manages the model weights, memory allocation (VRAM), and the API layer on your machine. When you \"run\" a model, Ollama loads the quantized version of that LLM into your GPU's memory. Since OpenAI doesn't release the weights for their \"Codex\" or \"GPT-5\" level models, you can't just `ollama run gpt-5`\n\n. You have to use open-weights models that have been trained on similar massive repositories of GitHub code.\n\n### Why the \"GPT-5 Codex\" hype is misleading for local users\n\nEveryone is chasing the next big leap in reasoning, but for those of us actually shipping code, the delta between a closed-source giant and a tuned local model is shrinking. I tried running a few \"frontier\" open-source models last Tuesday to see if they could handle a complex React state management bug that usually requires [Claude](/en/tags/claude/) 3.5 or a GPT-4o level of intelligence.\n\nThe reality? If you have 64GB of RAM and a decent NVIDIA card, you aren't missing much by staying local. You're trading a tiny bit of \"creative\" reasoning for zero latency and total privacy.\n\n### Setting up a local coding powerhouse with Ollama\n\nIf you're tired of API credits and want a local setup that feels like a high-end coding assistant, here is the actual workflow.\n\n1. **Install the engine**: Download Ollama from their site. It's a single binary.\n\n2. **Pull a coding specialist**: Don't just use Llama 3. Use a model actually trained for syntax.\n\n`ollama run deepseek-coder-v2`\n\n(This is currently the king of open-weights coding).\n\n3. **Connect to your IDE**: This is where the magic happens. Use the Continue.dev plugin in VS Code. Point the provider to `ollama`\n\nand the model to `deepseek-coder-v2`\n\n.\n\nNow you have an autocomplete and chat experience that doesn't send your proprietary codebase to a server in California.\n\n### Performance benchmarks: Local vs. Cloud\n\nI ran a test on my RTX 3090 (24GB VRAM) comparing a local DeepSeek-Coder-V2 (Lite) against a cloud-based GPT-4o call for a Python FastAPI boilerplate task.\n\n| Metric | Local (Ollama + DeepSeek) | Cloud (GPT-4o) |\n\n| :--- | :--- | :--- |\n\n| Time to First Token | ~0.2s | ~1.1s |\n\n| Tokens per second | 45 t/s | 60 t/s |\n\n| Privacy | 100% Local | Data sent to OpenAI |\n\n| Cost | $0 (Electricity) | Per Token |\n\n| Hallucination Rate | Slightly higher on obscure libs | Very low |\n\nThe speed is the wild part. Once the model is cached in VRAM, the response is nearly instantaneous. It makes the \"cloud wait\" feel like an eternity.\n\n### Handling the memory wall\n\nThe biggest gripe with running local LLMs is the VRAM. If you try to run a model that's too large, Ollama will offload layers to your system RAM.\n\nSuddenly, your 45 tokens/sec drops to 2 tokens/sec. It's agonizing.\n\nTo avoid this, always check the model size. A 7B parameter model usually fits in 8GB of VRAM (with 4-bit quantization). If you're pushing into the 30B+ range, you need serious hardware or you need to look into [AI Coding](/en/category/ai-coding/) strategies that utilize smaller, distilled models. I've found that for 90% of boilerplate, a highly quantized 7B model is plenty.\n\n### Integrating local models into a professional workflow\n\nJust running a chat box is boring. The real win is using these models as agents. By using the Model Context Protocol ([MCP](/en/tags/mcp/)), you can give your local Ollama instance the ability to read your local files, execute shell commands, and actually *test* the code it writes.\n\nI spent three hours last Friday trying to automate a migration script. Doing it via a web browser was a nightmare of copy-pasting. Switching to a local LLM integrated directly into the terminal via Ollama meant the AI could see the error logs in real-time.\n\nIf you're struggling to find the right configuration or want to see how others are chaining these tools together, checking out [Resources](/en/category/resources/) can save you a few hours of tinkering.\n\n### The \"Prompt Engineering\" gap\n\nHere is a truth most people miss: local models are more sensitive to prompts than GPT-4. If you use a lazy prompt like \"fix this code,\" a local model might just rewrite the whole file and hallucinate a library that doesn't exist.\n\nYou have to be explicit.\n\nInstead of \"Fix this,\" try:\n\n\"Analyze the following TypeScript function for race conditions. Return only the corrected code block. Do not explain your reasoning unless there is a breaking change.\"\n\nThe precision of your prompt determines whether your local LLM feels like a junior dev or a senior architect.\n\n### Joining the movement\n\nThe shift toward local-first AI isn't just about saving money; it's about ownership. When you run your stack on your own metal, you aren't subject to \"model drift\" where a provider updates the weights and suddenly your perfectly tuned prompts stop working.\n\nFor those who want to dive deeper into this ecosystem, the [PromptCube homepage](/en/) is a great starting point to see how a community of developers is optimizing these workflows. You can join the community, share your `.ollama`\n\nconfigs, and find out which quantized versions of models are actually performing well on specific hardware.\n\nStop paying for subscriptions you barely use. Install Ollama, pull a coding model, and start building.\n\n[Next Treating LLMs like search engines is the fastest way to get →](/en/threads/6699/)\n\n[a guide to making money with AI](https://tanyan888.com/), with plenty of directly applicable cases.\n\n## All Replies （0）\n\nNo replies yet — be the first!", "url": "https://wpnews.pro/news/can-i-run-a-gpt-5-codex-review-locally-using-ollama", "canonical_source": "https://promptcube3.com/en/threads/6713/", "published_at": "2026-08-17 18:48:09+00:00", "updated_at": "2026-08-17 19:14:09.897552+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "developer-tools", "ai-tools"], "entities": ["Ollama", "DeepSeek-Coder-V2", "CodeLlama", "OpenAI", "GPT-4o", "Continue.dev", "VS Code", "RTX 3090"], "alternates": {"html": "https://wpnews.pro/news/can-i-run-a-gpt-5-codex-review-locally-using-ollama", "markdown": "https://wpnews.pro/news/can-i-run-a-gpt-5-codex-review-locally-using-ollama.md", "text": "https://wpnews.pro/news/can-i-run-a-gpt-5-codex-review-locally-using-ollama.txt", "jsonld": "https://wpnews.pro/news/can-i-run-a-gpt-5-codex-review-locally-using-ollama.jsonld"}}