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The best open-weight model for local use is the one that fits your hardware and workload—not simply the one with the largest parameter count or highest published score. For edge devices, consider Gemma 4 E2B or E4B; for desktop and workstation use, compare larger Gemma 4 variants, OpenAI’s gpt-oss models, and DeepSeek-R1 distilled checkpoints. Check the exact model, runtime support, quantization, and license before downloading or deploying it.
Which local model should you start with?
Use the model family’s intended hardware tier as a first filter, then test the exact checkpoint on the tasks you care about. The model profiles below come from their publishers’ pages and model card; they are not a shared, independent hardware test.
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| Model option | What the source says about local use | What to check |
|---|---|---|
| Gemma 4 E2B / E4B | Google DeepMind positions these variants for mobile and IoT devices, including phones, Raspberry Pi, and Jetson Nano, and describes offline operation. The family is presented with audio and vision capabilities. | Confirm that your device and chosen runtime support the exact variant and inputs you need. The page’s performance language is not a guarantee for every device. |
| Gemma 4 12B / 26B / 31B | Google DeepMind positions these for consumer GPUs and local-first workstations. It lists reasoning, coding, agentic workflows, multimodal reasoning, function calling, and support for 140 languages across the family. | Memory needs depend on implementation, context length, and quantization. The official page does not specify one universal minimum. Google DeepMind’s Gemma 4 page |
| OpenAI gpt-oss-20b / gpt-oss-120b | OpenAI describes both as open-weight reasoning models for local desktops, laptops, and data centers. It links local guides for Ollama, LM Studio, Transformers, and vLLM. | The model names indicate parameter counts, not download size or required GPU memory. OpenAI states that gpt-oss is licensed under Apache 2.0. OpenAI’s open models page |
| DeepSeek-R1 and distilled variants | The model card lists a 671B-parameter R1 model and distilled Qwen- and Llama-based checkpoints ranging from 1.5B through 70B. It includes serving examples for distilled models with vLLM and SGLang. | Treat the 671B model and smaller distills as different hardware classes. The model card says R1’s repository and weights use MIT; Llama-derived distills retain their respective Llama licenses. Check the exact checkpoint and dependencies. DeepSeek-R1 model card |
These descriptions identify plausible starting points, not guaranteed minimum system requirements. A parameter count alone does not tell you how much memory a particular quantized build will need or how quickly it will run.
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Match the model to the work you want it to do
- General chat or coding: Start with a model your device can run comfortably, then test your own prompts and code tasks. Publisher claims and benchmark scores can help you shortlist candidates, but they do not establish which model will work best for your use.
- Math or scientific reasoning: Compare the same task types and scoring method across candidates when possible. A score from one benchmark suite should not be treated as directly comparable to a score from another.
- Tool use or agentic workflows: Check whether the exact checkpoint and runtime support the function-calling or tool-use path you plan to use. A family-level feature listing does not guarantee every checkpoint implements it the same way.
- Image or audio input: Verify that the specific model variant accepts the modality and that your chosen local runtime supports it. Do not infer modality support for every checkpoint from a family-wide description.
- Edge or offline use: Favor variants explicitly positioned for the device tier, and verify that the model and required runtime can operate on the device. Offline inference does not mean every part of setup is offline.
Check hardware and runtime before downloading
- Identify the target device and its usable memory. Note the accelerator, if any, and the memory available to the model. Do not convert a model’s parameter count into a precise VRAM requirement without a specification for the exact build.
- Choose a specific checkpoint and format. Look for the exact model version, quantized format if needed, and supported context length. Quantization and implementation affect memory use and behavior.
- Confirm runtime compatibility. Check that your local application supports that checkpoint, format, and target accelerator. OpenAI links guides for Ollama, LM Studio, Transformers, and vLLM; DeepSeek provides vLLM and SGLang serving examples for distilled variants. These routes do not establish support for every model in every runtime.
- Test representative tasks. Use prompts, files, and tool workflows like the ones you expect to use, and observe speed, output quality, and whether the model stays within available memory. Vendor benchmarks cannot substitute for this device-specific check.
- Read the license for the exact model and its dependencies. Terms can differ between a base model and a derived checkpoint. Confirm that the intended use—including commercial deployment or redistribution—is permitted by the applicable terms.
A consumer GPU may be relevant for larger local models, but the sources here do not establish a dependable minimum VRAM or a universally suitable card. Choose hardware only after identifying the exact model, runtime, format, and workload.
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How to interpret the published scores
The figures below are reported by the model publishers, not from a common independent test of all the families. Treat each result as evidence about that publisher’s stated evaluation, not as a definitive cross-model ranking.
| Publisher-reported result | Models and scores | Attribution and context |
|---|---|---|
| AIME 2024 | gpt-oss-120b: 96.6; gpt-oss-20b: 96.0 | OpenAI’s page, accessed October 11, 2026; benchmark labeled AIME 2024. OpenAI |
| AIME 2025 | gpt-oss-120b: 97.9; gpt-oss-20b: 98.7 | OpenAI’s page, accessed October 11, 2026; benchmark labeled AIME 2025. OpenAI |
| Arena AI text | Gemma 4 31B IT Thinking: 1452; Gemma 4 26B A4B IT Thinking: 1441 | Google DeepMind’s 2026 page labels the results “As of 4/2/26.” Google DeepMind |
| AIME 2026 | Gemma 4 31B IT Thinking: 89.2%; Gemma 4 26B A4B IT Thinking: 88.3% | Reported by Google DeepMind in 2026. Google DeepMind |
| LiveCodeBench v6 | Gemma 4 31B IT Thinking: 80.0%; Gemma 4 26B A4B IT Thinking: 77.1% | Reported by Google DeepMind in 2026. Google DeepMind |
Scores from different benchmark suites answer different questions; they do not, by themselves, show which model will be faster, more accurate, or more useful on your hardware.
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What local use does—and does not—mean for privacy
When inference runs on your own hardware, that inference can stay on your device. But downloading models, updating software, using integrations, or connecting to external services may still involve network traffic. To assess privacy, check the entire application path, including the runtime and any tools or services you connect; the phrase “runs locally” alone is not a complete privacy guarantee.
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