[
    {
        "role": "user",
        "content": "what%20are%20top%20llm%20models%20i%20can%20run%20on%20nvidia%20rtx%20pro%202000%20with%208%20gb%20vram%2C%20particularly%20for%20coding%20in%20python%20or%20bash"
    },
    {
        "role": "assistant",
        "content": "Here are some great LLMs you can run locally with your NVIDIA RTX PRO 2000 (8 GB VRAM), especially for coding in Python or Bash. The key is to choose smaller models (3B\u20138B parameters) or heavily quantized versions so they fit well in VRAM and still give usable performance. I'll also cover quantization, tools, speed expectations, and tips so you pick what works best.\n\n---\n\n## What to aim for\n\n| Component | Why it matters on an 8 GB GPU |\n|---|---|\n| Model size (parameters) | 7\u20138 B models are about the upper limit with quantization; 3\u20135 B models are much easier. ([runyard.dev](https://www.runyard.dev/blog/how-much-vram-to-run-local-llms?utm_source=openai)) |\n| Quantization | Reduces memory footprint massively. Q4_K_M, Q5_K_M, Q8 are common. Without it, even 8 B models may not fit. ([promptquorum.com](https://www.promptquorum.com/local-llms/fastest-local-llms-low-end-pcs?utm_source=openai)) |\n| Context window (KV cache) | Bigger prompts/use of history needs more VRAM. Shorter context means more headroom. ([sitepoint.com](https://www.sitepoint.com/optimizing-local-llms-low-end-hardware-8gb/?utm_source=openai)) |\n| Speed (tokens/sec) | For coding needs, you want ~20\u201360 tokens/sec. Heavily quantized 7B/8B or 3B models can do that. ([promptquorum.com](https://www.promptquorum.com/local-llms/fastest-local-llms-low-end-pcs?utm_source=openai)) |\n\n---\n\n## Top model choices for coding\n\nHere's a shortlist of excellent models you can run:\n\n| Model | Params | Quantization | Approx. VRAM use | Coding/Usage Strengths |\n|---|---|---|---|---|\n| **Qwen 3 8B Q4_K_M** | 8 B | Q4_K_M | ~5.2 GB | Best blend of quality and speed for Python/Bash autopilot-style usage. Strong code completion and multi-step reasoning. ([llmhardware.io](https://llmhardware.io/guides/best-llm-for-8gb-vram?utm_source=openai)) |\n| **Mistral 7B Q4** | 7 B | Q4_K | ~7.7 GB | Excellent for more nuanced code generation when prompts are short. More VRAM tight. ([llmhardware.io](https://llmhardware.io/guides/best-llm-for-8gb-vram?utm_source=openai)) |\n| **Llama 3.1 / 3.2 8B Q4_K_M** | 8 B | Q4_K_M | ~5\u20136 GB | Very capable for coding \u2014 good prompt understanding, reliable performance. ([sitepoint.com](https://www.sitepoint.com/best-local-llm-models-2026/?utm_source=openai)) |\n| **Phi-4 Mini 3.8B Q4** | ~3.8 B | Q4_K_M | ~2\u20133 GB | Fast, lightweight; useful for building smaller tools, exploring code, snippets. Less depth but burly efficiency. ([promptquorum.com](https://www.promptquorum.com/local-llms/fastest-local-llms-low-end-pcs?utm_source=openai)) |\n| **Gemma 4B CodeGemma / 7B** | 4 or 7 B | Q8 or Q4_K | depends on size | Tailored for code completion; good mid tier between speed and capability. ([en.wikipedia.org](https://en.wikipedia.org/wiki/Gemma_%28language_model%29?utm_source=openai)) |\n\n---\n\n## Tools & formats\n\n- **llama.cpp / llama.cpp-based tools**: Support GGUF format with quantization to Q4, Q5, Q8, etc. Very useful for VRAM-constrained GPUs. ([sitepoint.com](https://www.sitepoint.com/optimizing-local-llms-low-end-hardware-8gb/?utm_source=openai))  \n- **Ollama**: Simplifies pulling models, managing quantization variants, and running them locally. Fits the workflows above well. ([llmhardware.io](https://llmhardware.io/guides/best-llm-for-8gb-vram?utm_source=openai))\n\n---\n\n## Coding-specific performance metrics\n\nHere\u2019s what you can expect with those setups:\n\n- **HumanEval accuracy**: Qwen3 8B gets around **72%** on coding-specific benchmarks while using ~5 GB VRAM. ([promptquorum.com](https://www.promptquorum.com/local-llms/best-local-llms-for-coding?utm_source=openai))  \n- **Token generation speed**:  \n\u2002\u2002\u2022 For 7B/8B Q4 models \u2192 ~25\u201335 tokens/sec on good 8 GB GPUs ([llmhardware.io](https://llmhardware.io/guides/best-llm-for-8gb-vram?utm_source=openai))  \n\u2002\u2002\u2022 For lighter 3\u20134B models \u2192 ~50\u201370+ tokens/sec, much faster interactive feedback. ([openclawdc.com](https://openclawdc.com/blog/best-local-llms-8gb-ram/?utm_source=openai))\n\n---\n\n## Practical trade-offs\n\n| Trade-off | What you lose | What you gain |\n|---|---|---|\n| Heavier quantization (Q2/Q3) or tiny models | Loss of output quality\u2014less nuance, possibly more bugs in generated code | Much more room in VRAM, better speed |\n| Using 7\u20138B models with short context | Limited prompt length, possibly worse for large projects | VRAM fits, stable performance |\n| Choosing 3\u20134B models | Lower \"understanding\" of complex tasks | Enough for many scripting needs, much faster |\n\n---\n\n## Recommendations for your RTX PRO 2000 (8 GB VRAM)\n\nTo get the most out of your card for coding in Python or Bash:\n\n1. Start with **Qwen 3 8B Q4_K_M**. Use it for daily completions, scripting, debugging help. It leaves a fair bit of VRAM for terminal, editor, etc.\n2. Use **Mistral 7B Q4** only when your prompt is small. It\u2019s richer but more demanding.\n3. Keep **Phi-4 Mini 3.8B** for quick tasks or when you want highest speed (e.g. iterating loops, experimenting).\n4. If you want lower VRAM usage, drop to **3B** models with Q5 or Q8 quantization.\n5. Use *shorter context windows* (2048\u20134096 tokens unless you're very sure you've got room). Extra KV cache eats VRAM fast. ([sitepoint.com](https://www.sitepoint.com/optimizing-local-llms-low-end-hardware-8gb/?utm_source=openai))\n\n---\n\nIf you want, I can match this advice to your actual setup (OS, available system RAM, Python environment) and recommend the fastest possible configuration or pre-built models + tools for you. Want me to put that together?"
    }
]