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Choose hardware by matching its available GPU VRAM or Apple unified memory to the Gemma 4 model and precision you plan to run, then leave room for context and runtime overhead. Google’s published figures are a useful starting point—not a guarantee that a model will fit or run well in a complete local-agent setup.
Start with the model and its memory footprint
Gemma 4 has five sizes: E2B, E4B, 12B, 26B A4B, and 31B. Google positions the smaller E models for edge and on-device use, while the larger models target consumer GPUs and workstations. The table below gives Google’s approximate GPU or TPU memory estimates for loading model weights at three precisions.
| Gemma 4 model | BF16 (16-bit) | SFP8 (8-bit) | Q4_0 (4-bit) |
|---|---|---|---|
| E2B | 11.4 GB | 5.7 GB | 2.9 GB |
| E4B | 17.9 GB | 8.9 GB | 4.5 GB |
| 12B | 26.7 GB | 13.4 GB | 6.7 GB |
| 26B A4B | 57.7 GB | 28.8 GB | 14.4 GB |
| 31B | 69.9 GB | 34.9 GB | 17.5 GB |
Google AI for Developers describes these as approximate requirements to load the weights, calculated from parameter count and quantization and including 20% overhead for loading additional things. They exclude supporting software and context-window memory, and may vary by inference tool and environment. See Google’s Gemma 4 model overview for the estimates and qualifications.
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Compare the estimate for your chosen model and precision with memory actually available to the inference workload. Context processing, the KV cache, the runtime, and other GPU use require additional memory. Google warns that larger context windows require significantly more VRAM on top of model weights. A machine that meets the table’s weight estimate may therefore still need a shorter context, a lower-memory configuration, or a smaller model.
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Understand the 26B A4B label
The “A4B” indicates that about 4 billion parameters are active per token, but it does not mean the model only occupies memory for 4 billion parameters. Google says all 26 billion parameters must be loaded for fast routing and inference, so size hardware against the full-model estimate.
Choose precision and size for the work you actually do
Lower-bit quantization reduces memory requirements and can make a larger model practical on a given system. It may also affect capability. Google’s run guide notes that quantized models can still perform well depending on task complexity, but does not offer a universal quality guarantee. A more capable model or higher precision generally costs more memory, processing, and power; the right trade-off depends on your tasks.
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- For compact on-device use, compare E2B and E4B against the memory available on your device.
- For a larger model in a constrained memory budget, compare its Q4_0 estimate with available memory, while accounting for context and software.
- If your tasks need longer prompts, substantial tool definitions, files, or extended conversation history, budget more than the weight estimate; the exact extra agent memory requirement is not specified in Google’s figures.
Model choice can also depend on modality. Google’s model card lists image input for all five sizes. E2B, E4B, and 12B support audio; 26B A4B and 31B are listed for text and image only. Check the Gemma 4 model card against the input types your workflow needs.
Match the memory architecture to the target model
Discrete-GPU systems
For a discrete GPU, compare its available VRAM—not just the model’s advertised capacity—with the selected precision’s estimate. A 24 GB VRAM graphics card is a reasonable category to consider for larger Q4_0 variants: it exceeds the published 14.4 GB estimate for 26B A4B and 17.5 GB for 31B. That comparison is an inference from Google’s weight table, not a tested configuration or a guarantee at maximum context. It also says nothing about which card is best value.
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Apple Silicon systems
Apple Silicon uses unified memory rather than a separate pool of GPU VRAM. Compare memory available to the model with the same precision-specific estimates, leaving room for the operating system, runtime, context, and other applications. Google lists MLX among the local inference options for Apple Silicon, but runtime and model-format support should be checked for the particular setup.
Workstations and other devices
For any workstation or edge device, the same decision applies: identify memory available to the inference backend, then compare it with the model’s weight estimate plus the additional needs of context and software. A model’s fit in memory alone does not establish acceptable generation speed or suitability for an agent workflow.
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Check local runtime and agent compatibility
Gemma 4’s model card documents native function calling and agentic capabilities, but an agent setup also depends on whether the inference framework can load the chosen model format and expose an endpoint the agent can use. Google lists LM Studio and Ollama for local chat, llama.cpp and LiteRT-LM for local or edge inference, and MLX for Apple Silicon. Confirm support for the desired format, hardware backend, and integration before settling on hardware. Google’s Gemma 4 run guide describes the framework options.
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Google AI Edge describes LiteRT-LM’s serve command as exposing an OpenAI-compatible local endpoint. Google names OpenClaw, Hermes, OpenCode, Pi, Continue, and Aider as examples of tools that can connect. This is an integration example, not a promise of equal support across operating systems and model variants or of reliable agent behavior in every workflow. See Google AI Edge’s LiteRT-LM deployment page.
A practical hardware-selection sequence
- List the workload. Note whether you need text, image, or audio input; how long prompts and conversation histories are likely to be; and which agent or coding tool you want to connect.
- Pick candidate models. Start with the smallest Gemma 4 size likely to meet the task, then consider a larger model if your quality needs warrant its added resource cost.
- Choose a precision. Compare BF16, SFP8, and Q4_0 in the table. Treat lower-bit options as a memory-versus-capability trade-off, not as guaranteed equivalents.
- Compare available memory. Check GPU VRAM or Apple unified memory against the selected estimate, then reserve additional capacity for context, the KV cache, supporting software, and other GPU use.
- Verify the software path. Confirm that your chosen runtime supports the model format and your hardware backend, and that it offers an endpoint or interface compatible with your agent.
- Test the actual workload before committing. Check the chosen model, context length, runtime, and agent together. Google’s published weight estimates do not establish end-to-end speed, maximum practical context, or agent reliability for a particular computer.
What published figures do—and do not—tell you
Google’s memory table supports comparing model sizes and quantizations, but it is not a consumer GPU benchmark or a complete agent deployment measurement. Google AI Edge publishes selected LiteRT-LM performance examples for E2B and E4B on named devices and backends; those results are specific to that implementation and hardware, not universal speed claims. The available official evidence does not establish a best-value GPU, a universally sufficient model size, or a tested end-to-end hardware recommendation.
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