# Can I run a GPT-5 Codex review locally using Ollama? > Source: > Published: 2026-08-17 18:48:09+00:00 # Can I run a GPT-5 Codex review locally using Ollama? [DeepSeek](/en/tags/deepseek/)-Coder-V2 or CodeLlama via Ollama to get a similar experience. The 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` . You have to use open-weights models that have been trained on similar massive repositories of GitHub code. ### Why the "GPT-5 Codex" hype is misleading for local users Everyone 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. The 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. ### Setting up a local coding powerhouse with Ollama If you're tired of API credits and want a local setup that feels like a high-end coding assistant, here is the actual workflow. 1. **Install the engine**: Download Ollama from their site. It's a single binary. 2. **Pull a coding specialist**: Don't just use Llama 3. Use a model actually trained for syntax. `ollama run deepseek-coder-v2` (This is currently the king of open-weights coding). 3. **Connect to your IDE**: This is where the magic happens. Use the Continue.dev plugin in VS Code. Point the provider to `ollama` and the model to `deepseek-coder-v2` . Now you have an autocomplete and chat experience that doesn't send your proprietary codebase to a server in California. ### Performance benchmarks: Local vs. Cloud I 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. | Metric | Local (Ollama + DeepSeek) | Cloud (GPT-4o) | | :--- | :--- | :--- | | Time to First Token | ~0.2s | ~1.1s | | Tokens per second | 45 t/s | 60 t/s | | Privacy | 100% Local | Data sent to OpenAI | | Cost | $0 (Electricity) | Per Token | | Hallucination Rate | Slightly higher on obscure libs | Very low | The 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. ### Handling the memory wall The 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. Suddenly, your 45 tokens/sec drops to 2 tokens/sec. It's agonizing. To 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. ### Integrating local models into a professional workflow Just 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. I 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. If 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. ### The "Prompt Engineering" gap Here 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. You have to be explicit. Instead of "Fix this," try: "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." The precision of your prompt determines whether your local LLM feels like a junior dev or a senior architect. ### Joining the movement The 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. For 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` configs, and find out which quantized versions of models are actually performing well on specific hardware. Stop paying for subscriptions you barely use. Install Ollama, pull a coding model, and start building. 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