Yes—some open-weight language models can run locally on small computers, but the model, memory, runtime, context length, and acceptable response speed all matter. A Raspberry Pi 5 with 8 GB of RAM has a published benchmark for Gemma 4 E2B; Apple Silicon Macs have a documented local-model stack, though larger models can require far more memory.
What “running an LLM locally” requires
Local inference means the computer loads a model and generates responses on the device rather than sending each prompt to a hosted model. It does not mean every model will fit, run at a useful speed, or work with every runtime.
Before choosing a computer, check four factors together:
- Memory fit: account for the model representation, runtime overhead, and the context you want to keep active—not just the model’s advertised parameter count.
- Decode speed: how quickly generated text appears after the prompt has been processed.
- Prefill speed: how quickly the model processes the prompt or supplied context. This becomes important with long inputs and repeated agent requests.
- Runtime support: confirm that the software supports the device, model architecture, and interface you need.
Task matters too: a compact model may be adequate for short prompts or simple edge tasks, while more demanding reasoning or coding may call for a larger model. The available published figures do not establish a controlled, cross-platform comparison of answer quality.
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What a Raspberry Pi 5 can run
Raspberry Pi reports running Gemma 4 E2B on a Raspberry Pi 5 with 8 GB of RAM. Its results show how much the runtime and model packaging can affect a particular configuration; they are publisher-reported benchmarks, not independent testing.
| Gemma 4 E2B configuration | Prefill speed | Decode speed | Peak memory |
|---|---|---|---|
| LiteRT-LM (QAT) | 99 tokens/sec | 9 tokens/sec | 1,432 MB |
| llama.cpp (Q4_0 GGUF) | 24 tokens/sec | 4 tokens/sec | 4,406 MB |
For both rows, Raspberry Pi’s test used an 8 GB Raspberry Pi 5, four CPU threads, 1,024 prefill tokens, and 256 decode tokens. The llama.cpp model file was gemma-4-E2B-it-Q4_0.gguf; LiteRT-LM used gemma-4-E2B-it.litertlm. Because both runtime and model format differ, this is not a controlled test of quantization alone. Raspberry Pi’s Gemma and LiteRT benchmark provides the full context.
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The same Raspberry Pi article reports a separate, much smaller Gemma 3 270M example on the Pi 5 8 GB: LiteRT-LM reached 433.17 prefill tokens/sec and 22.58 decode tokens/sec, with a 278 MB model and 680 MB peak memory. Those results describe Gemma 3 270M, not Gemma 4 E2B.
How Apple Silicon Macs compare
Apple describes a local workflow for Apple Silicon built from MLX, MLX-LM, and MLX-LM Server. MLX handles computation and memory management; MLX-LM loads and runs models and can quantize or fine-tune them; MLX-LM Server exposes a local OpenAI-compatible HTTP endpoint for applications and agents. Apple recommends beginning with a small model to validate a setup. See Apple’s WWDC26 presentation on running local agentic AI on Mac with MLX.
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Ollama also describes an Apple Silicon preview powered by MLX. For its featured Qwen3.5-35B-A3B coding workflow, Ollama says to use a Mac with more than 32 GB of unified memory. That threshold applies to the named model and setup; it is not a general minimum for Ollama or local inference. Ollama’s announcement identifies the workflow and its preview status.
Choose a setup by workload, not by model label
- Pick a target task. Decide whether you need short local prompts, processing of long documents, coding assistance, or an application endpoint for an agent.
- Choose a small model and supported runtime. Start with a model documented for the device. For a Mac, Apple’s MLX stack is one documented path; llama.cpp describes local command-line chat and an OpenAI-compatible server for laptops, desktops, or servers.
- Check memory with your intended context. Leave room for runtime needs and the active context, then verify actual peak use on the target machine. Results from one model or format do not guarantee another will fit.
- Measure both prompt handling and generation. Record prefill and decode speeds for the exact model, runtime, hardware, and workload. A decode figure alone does not show how long a large prompt takes to process.
- Validate before relying on the setup. Test representative prompts and the interface your application will use. If memory use or speed is unsuitable, try a smaller model or a different supported runtime and recheck.
What the published examples do—and do not—show
The Raspberry Pi figures establish that a compact model can run locally on a Pi 5 8 GB under specified benchmark conditions. Apple’s documentation establishes a software route for local models on Apple Silicon. Neither source set establishes a universal hardware minimum, a best computer for every model, or a comprehensive comparison of small computers.
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In particular, do not infer that the Pi benchmark predicts performance on another board or that Ollama’s more-than-32-GB guidance applies to every model. Treat published speeds as configuration-specific, and verify the exact combination of device, model, runtime, and context you intend to use.
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