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Start with Gemma 4 E2B and treat a one-chip TPU v5e run as a compatibility test, not a guaranteed turnkey deployment. Google’s MaxText guide documents E2B inference with ici_tensor_parallelism=1, but the official material does not verify a specific quantized Gemma 4 checkpoint running end to end on exactly one TPU v5e chip. The first decision is therefore not just which model to use, but whether its checkpoint format is supported by the TPU runtime you intend to use.
First, define what “single TPU v5e” means
A single TPU v5e chip is not the same thing as a single-host TPU v5e node. Google’s older JetStream tutorial uses Gemma 7B on single-host TPU v5e nodes; it is not a Gemma 4, one-chip recipe. Google Cloud now identifies vLLM TPU as its recommended GKE serving path, but that recommendation does not establish that every Gemma 4 quantized checkpoint works with it.
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The closest documented starting point for a one-chip experiment is MaxText’s Gemma 4 E2B inference example, which sets ici_tensor_parallelism=1 through its vLLM adapter. That is a software-path example, not proof of a successful quantized QAT checkpoint load on exactly one TPU v5e chip.
Choose a model that leaves room for runtime memory
For an initial feasibility test, E2B is the most conservative choice. Google’s approximate Q4_0 loading estimates provide a useful comparison, but they are not total runtime memory guarantees. Google includes a 20% allowance for additional loading needs; software and context-dependent KV-cache memory are excluded. Longer prompts and generations increase memory demand.
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| Gemma 4 variant | Approximate Q4_0 inference loading memory | What to keep in mind |
|---|---|---|
| E2B | 2.9 GB | Best starting point for testing one-chip feasibility; runtime and KV cache require additional memory. |
| E4B | 4.5 GB | Consider only if its additional capacity is needed and the target runtime has enough headroom. |
| 12B | 6.7 GB | Loading estimate excludes software and context-dependent KV cache. |
| 26B A4B | 14.4 GB | All experts must be loaded even though only four billion parameters activate per token. |
| 31B | 17.5 GB | Loading estimate excludes software and context-dependent KV cache. |
These estimates are from Google’s Gemma 4 model overview. Begin with a short context or a low max_model_len in the selected serving configuration, then increase it only after observing memory use on the target setup. Do not treat the Q4_0 figure as proof that a model fits or runs on a TPU: it describes approximate inference loading memory, not TPU runtime compatibility.
Match the quantized checkpoint to the runtime
Quantized formats are not interchangeable. Google’s QAT guidance routes Q4_0 GGUF to llama.cpp or LM Studio, while compressed-tensor w4a16-ct checkpoints are designated for vLLM or SGLang. Google also provides unquantized QAT weights intended as inputs for conversion into other formats.
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- For Q4_0 GGUF: Google identifies llama.cpp and LM Studio as the target runtimes. The reviewed instructions do not establish that this format can be loaded directly by the MaxText TPU example.
- For compressed tensors: Google identifies vLLM and SGLang as target runtimes. Confirm that the specific TPU backend and installed runtime support the checkpoint before proceeding.
- For unquantized QAT weights: Google describes these as inputs for conversion to other formats. Conversion does not itself establish that the resulting checkpoint is supported on one TPU v5e chip.
QAT, or quantization-aware training, simulates quantization during training to limit quality loss when weights are compressed. That property does not guarantee compatibility with a particular TPU serving stack. In particular, do not assume Google’s compressed-tensor or GGUF artifact is the same as the Orbax-style MaxText-compatible checkpoint used by the MaxText inference example.
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MaxText’s Gemma 4 instructions describe converting model weights into a MaxText-compatible checkpoint in Google Cloud Storage, then loading that checkpoint for inference through the MaxText vLLM adapter. The documented path requires accepting the Gemma license through Hugging Face and authenticating with an HF_TOKEN. Use the current MaxText guide for the exact command syntax and required software versions; the documented settings below describe the relevant E2B configuration rather than asserting an unverified quantized one-chip command.
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Convert the checkpoint
- Accept Google’s Gemma license in Hugging Face and authenticate using the token mechanism required by the guide.
- Use the MaxText Gemma 4 conversion procedure, selecting
model_name=gemma4-e2band the Hugging Face model path for the checkpoint you intend to convert. - For the small E2B/E4B MaxText variants, the guide’s conversion example sets
use_multimodal=falseandscan_layers=false. It notes that multimodal is currently gated off for these variants in this MaxText path. - Write the converted weights to the Google Cloud Storage location that the inference step will use.
Run inference with one-chip parallelism
For offline inference, MaxText’s guide uses the maxtext.inference.vllm_decode entry point, the converted checkpoint, and an upstream tokenizer path. Its E2B example sets ici_tensor_parallelism=1 and scan_layers=False; the vLLM path requires an unscanned checkpoint. Those settings document how the MaxText example is configured. They do not show that a particular quantized QAT checkpoint has been validated with that path.
For E2B or E4B instruction-tuned checkpoints, MaxText recommends providing a system prompt and sampling with temperature 1.0, top-p 0.95, and top-k 64. Preserve the complete stop-token set specified by the guide; dropping stop tokens can change when generation ends. Use the current guide’s full invocation and checkpoint-specific settings rather than assembling a command from these parameter names alone.
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Validate on the actual TPU before scaling up
The official material reviewed does not establish an end-to-end combination of a named quantized Gemma 4 checkpoint, a specific MaxText/vLLM TPU version, and exactly one TPU v5e chip. Validate the precise artifact and runtime on the TPU configuration you will use before treating it as supported.
- Confirm the hardware target. Distinguish one chip from a multi-chip or single-host TPU configuration; the latter does not demonstrate one-chip operation.
- Check format support. Verify the checkpoint format against the installed runtime and TPU backend. A format’s association with vLLM or SGLang generally is not, on its own, proof of support on the specific TPU path.
- Use the checkpoint form the selected path expects. If following MaxText, convert weights to its compatible checkpoint and meet its unscanned-checkpoint requirement for vLLM inference.
- Start small. Use E2B, a short context, and a short generation. Confirm that the model loads and produces output before raising context length or trying a larger variant.
- Record the working configuration. Note the checkpoint identity and format, runtime and backend versions, TPU configuration, context setting, and whether loading and generation both succeed. This makes a later compatibility failure easier to isolate.
If loading fails, first check for a checkpoint/runtime format mismatch and whether the selected MaxText path received an unscanned, MaxText-compatible checkpoint. If it loads but runs out of memory as context grows, reduce context or generation length before considering a larger model. No official named throughput figure or verified one-chip Gemma 4 quantized benchmark is established by these instructions, so measure performance on the exact configuration rather than inferring it from the loading estimates.
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Choose a deployment route for the job
Google Cloud identifies GKE, GCE, and Vertex AI as TPU deployment routes for Gemma 4, while recommending vLLM TPU for serving in GKE. These are distinct infrastructure choices, not interchangeable recipes: select the route based on how you need to provision and operate TPU compute, then verify its current model and checkpoint support. The MaxText/vLLM steps above are a specific implementation path, not a claim that all managed or orchestrated routes accept the same checkpoint or configuration.
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