The Tool Desk
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There is an important distinction: a “single-host TPU v5e node” is not necessarily one TPU chip. The older Google Cloud JetStream example uses Gemma 7B on single-host v5e nodes, so it is not a verified recipe for Gemma 4 on one chip.
Choose a model that fits the experiment
For a single-chip feasibility test, begin with E2B. Consider E4B if its capabilities better fit your task and you can validate that the selected runtime has enough memory. The figures below are Google’s approximate Q4_0 inference loading estimates, not total runtime-memory guarantees.
| Gemma 4 variant | Approximate Q4_0 loading memory | What to keep in mind |
|---|---|---|
| E2B | 2.9 GB (Google AI for Developers, accessed 2026) | Smallest listed variant; a sensible first single-chip candidate. |
| E4B | 4.5 GB (Google AI for Developers, accessed 2026) | More loading memory than E2B; validate runtime and context headroom. |
| 12B | 6.7 GB (Google AI for Developers, accessed 2026) | Higher loading requirement than the small variants. |
| 26B A4B | 14.4 GB (Google AI for Developers, accessed 2026) | All experts must be loaded even though four billion parameters activate per token. |
| 31B | 17.5 GB (Google AI for Developers, accessed 2026) | Largest listed loading estimate. |
Google says these estimates include a 20% allowance for additional loading needs, but exclude supporting software and context-dependent KV cache. Longer prompts and generations require more KV-cache memory. Start with short prompts and a conservative context limit, then increase it only after observing memory use on your actual setup.
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Do not confuse the separate Gemma 4 E2B text-only mobile checkpoint without Per-Layer Embeddings, which Google describes as needing less than 1 GB, with the Q4_0 TPU loading estimate. They are different configurations.
Choose a quantization format that matches the runtime
“Quantized Gemma 4” does not identify one interchangeable file format. Google’s QAT documentation routes Q4_0 GGUF to llama.cpp or LM Studio, and compressed-tensor (w4a16-ct) checkpoints to vLLM or SGLang. Google also describes unquantized QAT weights as inputs that can be converted to other formats.
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- Q4_0 GGUF: Google identifies this format with llama.cpp and LM Studio. Do not assume it is the input expected by MaxText’s vLLM TPU path.
- Compressed tensors (
w4a16-ct): Google identifies these checkpoints with vLLM and SGLang. That routing alone does not establish compatibility with the specific TPU backend and MaxText checkpoint workflow you intend to use. - Unquantized QAT weights: These are a distinct option for conversion into another format; they are not themselves proof that a particular converted checkpoint will run on one v5e chip.
Google describes quantization-aware training as simulating quantization during training to minimize quality loss when a model is compressed. That is a model-quality rationale, not a guarantee of runtime or hardware compatibility.
Use the documented MaxText Gemma 4 path where it fits
MaxText documents Gemma 4 inference through its vLLM adapter. Its guide describes converting model weights to a MaxText-compatible checkpoint, storing that checkpoint in Google Cloud Storage, and then loading it for inference. This is the relevant documented TPU software path in the supplied official guidance; it should not be represented as a direct, verified route from every QAT checkpoint format.
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1. Prepare access and convert the checkpoint
- Accept the Gemma license through Hugging Face and authenticate with an
HF_TOKEN, as required by the MaxText guide. - Follow the guide’s Gemma 4 conversion procedure to write a MaxText-compatible checkpoint to Google Cloud Storage. For E2B, its example selects
model_name=gemma4-e2b, setsuse_multimodal=falseandscan_layers=false, and supplies a Hugging Face model path. - Confirm that the checkpoint you chose is actually supported by the installed MaxText/vLLM TPU path. The reviewed guidance does not explicitly establish a direct conversion-and-run path for a specific quantized QAT checkpoint on exactly one TPU v5e chip.
The small E2B/E4B variants use Per-Layer Embeddings and KV sharing. MaxText’s Gemma 4 instructions call for an unscanned checkpoint (scan_layers=false); the conversion example also disables multimodal support, which the guide says is currently gated off for these MaxText variants.
2. Run inference with one-chip parallelism
For offline inference, the guide uses the maxtext.inference.vllm_decode entry point with the converted checkpoint and an upstream tokenizer path. Its E2B example sets ici_tensor_parallelism=1 and scan_layers=False. Use the guide’s full invocation and configuration for your installed version rather than combining those settings with an arbitrary GGUF or compressed-tensor file.
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For E2B or E4B instruction-tuned checkpoints, MaxText recommends supplying a system prompt and using temperature 1.0, top-p 0.95, and top-k 64. Preserve the complete stop-token set from the guide; dropping stop tokens can change when generation ends.
Validate the actual one-chip setup
The official material reviewed documents one-chip parallelism in the MaxText E2B example, but does not demonstrate a complete run combining a particular quantized Gemma 4 QAT checkpoint, a specific MaxText/vLLM TPU version, and exactly one TPU v5e chip. Treat the workflow as a documented software path that requires compatibility and hardware validation.
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- Establish what “single TPU” means. Confirm whether your allocation is one chip or a single-host node containing TPU resources. A single-host configuration does not by itself mean one chip.
- Start with E2B and short inputs. Use a short context or prompt and avoid long generations during the first load test; context and KV-cache memory are outside Google’s model-loading estimate.
- Check checkpoint/runtime compatibility. Verify the selected checkpoint format against the exact installed MaxText, vLLM TPU backend, and hardware path. Do not infer support just because Google lists a format for vLLM generally.
- Observe loading and generation on the target. Confirm the checkpoint loads, the model produces output, and memory remains available for the chosen prompt and generation lengths. Expand context only after those checks.
- Record the tested combination. Note the model variant and checkpoint format, software versions, TPU allocation, context settings, and whether loading and generation succeeded. Without that reproduction, avoid claiming a successful one-chip run or quoting speed and throughput.
Choose a TPU deployment route deliberately
Google Cloud identifies GKE, GCE, and Vertex AI as TPU deployment routes for Gemma 4. The right choice depends on whether you need an orchestrated cluster, a virtual machine, or a managed platform; availability and setup details should be checked for the selected service. For serving LLMs on TPUs in GKE, Google Cloud now recommends vLLM. That recommendation does not make the older JetStream tutorial a Gemma 4 single-chip procedure: its example uses Gemma 7B and a single-host v5e node.
Use the MaxText instructions for the documented Gemma 4 conversion and inference flow, and use Google Cloud’s deployment documentation to choose where to run it. Keep the two questions separate: which deployment service provides your TPU resources, and whether your exact checkpoint/runtime combination is supported on one chip.
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