For a first local desktop-pet setup, use a runtime your pet app explicitly supports, start with a small quantized chat model, and keep context modest until you know the pet needs more conversation history. The right model and speed depend on your computer’s memory, graphics hardware, operating system, and the pet’s integration—not parameter count alone.
Check whether your desktop pet supports a local model
Before downloading a model, check the pet application’s documentation or settings for its supported local runtimes, API options, and operating systems. There is no universal integration: an app might connect to Ollama, accept a compatible API endpoint, or support a different runtime entirely. Confirm that path first, then choose a model the runtime can run.
Record your operating system, system RAM, and GPU memory (VRAM), or unified memory on a system that shares memory between the CPU and GPU. Also check how much free disk space you have. Those details determine which models and context settings are practical.
Choose a runtime and install a compact model
Use a runtime that fits the pet’s integration
Ollama is one beginner-friendly option: its desktop app is available for macOS and Windows, and it also supports command-line and API use. Google’s Gemma setup instructions include Ollama, and Google says Ollama and llama.cpp can run quantized Gemma models on a laptop or other small device without a GPU. That does not mean every pet app supports either runtime; verify compatibility with the pet first. See Ollama’s download page and Google’s Gemma documentation.
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Start with a quantized chat model
Choose a compact instruction-tuned or chat model that fits comfortably in available memory. Quantization reduces the memory and storage needed for model weights, making a model easier to run locally. Keep room for the operating system, the pet app, the runtime, and the context cache rather than selecting a model that only just loads.
Memory guidance varies by model. Ollama’s Llama 2 page gives general RAM rules of thumb of at least 8 GB for a 7B model, 16 GB for 13B, and 64 GB for 70B; it also says higher quantization levels require more memory. These figures apply to that model family’s guidance, not every modern model or every runtime. Compare the model’s actual quantized file size and memory use, not just its parameter count. Ollama’s Llama 2 page provides the example.
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Leave enough disk space
Model files can take substantial room: Ollama says storage needs can reach tens to hundreds of gigabytes. If your internal disk is short on space, an external SSD is an optional way to store model files; the cited guidance does not establish an ideal drive capacity, and storage on an SSD does not by itself guarantee faster inference. Check the runtime’s model-storage location and available space before downloading.
Set context to match the pet’s conversations
Context is the token budget for the prompt and retained conversation. A longer context can let the pet account for more of the interaction, but it also consumes more memory. Start with a modest setting, then increase it only if the pet’s actual use requires more history. Ollama’s app documentation specifically notes that increasing context requires more memory: Ollama’s app announcement.
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A model’s advertised maximum context is not automatically a sensible setting for your desktop. Model weights, the key-value (KV) cache used to track context, runtime overhead, and other running apps all compete for memory. Very long contexts can also depend on model-specific attention features. Ollama documents sliding-window and chunked attention mechanisms and cautions that an incompletely implemented attention layer can make output erratic or degraded over longer contexts. Use a runtime/model combination that supports the model’s intended attention behavior before substantially increasing context. See Ollama’s attention-feature documentation.
Do not copy context numbers from unrelated workflows. For example, Ollama’s January 23, 2026 launch article estimates about 23 GB of VRAM for GLM-4.7-Flash at 64,000 context and advises at least 64,000 context for the coding integrations listed there. That is a model- and workflow-specific example, not a target for a desktop pet. Ollama’s GLM-4.7-Flash article.
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Measure speed on your own computer
Model size alone cannot predict how quickly a local pet will respond. Hardware, runtime, model format, context length, and prompt all affect the experience. Separate the wait before the first visible response from the speed of generating the response: prompt processing and token generation are different parts of the wait.
- Choose a representative prompt. Use the same short prompt each time, then try a normal pet conversation that reflects how you will actually use it.
- Keep the test settings fixed. Record the model and quantization, context setting, runtime, and computer configuration. Avoid comparing results from different prompts or settings as though they were a controlled comparison.
- Record the experience. Note the time to the first visible response and, if the runtime reports it, approximate generation speed. Repeat the test after changing one setting at a time.
- Adjust for the bottleneck. If responses are too slow or the system runs short of memory, try a smaller model or a lower context setting. If the pet loses useful conversation history, test a larger context step while watching memory and response time.
Vendor measurements illustrate why speed figures need their conditions attached. In a September 23, 2025 Ollama report, Gemma 3 12B at 128K context on one GeForce RTX 4090 produced 85.54 generated tokens per second and used 21.4 GiB of VRAM under a newer scheduling system. The same article compares an earlier result on that setup of 52.02 tokens per second and 19.9 GiB of VRAM. These are Ollama’s configuration-specific measurements, not predictions for another model or computer. Ollama’s scheduling article.
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Compare settings by fit, not by a single ranking
When deciding whether to keep a model or change a setting, compare the factors that matter for your pet on your computer:
- Task quality: Does the model handle the pet’s real prompts and conversational style well enough?
- Memory and storage: What are the quantized model’s file size and peak memory use, including at the context setting you intend to use?
- Usable context: How much conversation history can the machine retain without leaving too little memory for the model and other apps?
- Responsiveness: How long until the first visible response, and what sustained generation speed does the runtime report?
- Compatibility: Does the pet support the runtime, and does that runtime implement the model’s intended attention behavior?
There is no universally best model for an unspecified desktop. The useful choice is the largest model that runs comfortably at the context your pet actually needs while still responding at an acceptable pace.
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