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What is the difference between QAT and PTQ?
Post-training quantization compresses a model after it has been trained. Quantization-aware training incorporates simulated quantization into training, giving the model an opportunity to adapt to the precision limits. Google describes this distinction in its Gemma 4 model overview.
Google’s launch article says: “While PTQ is already effective at preserving quality, our QAT results yield even higher overall quality compared to standard PTQ baselines.” That is Google’s summary of its results, not a guarantee for every QAT checkpoint, PTQ algorithm, bit width, task, or runtime. The reviewed sources do not provide a controlled, task-by-task Gemma 4 QAT-versus-PTQ quality table or a numerical quality advantage.
Which should you use?
Choose official QAT when a matching checkpoint is available
QAT is the practical first candidate when Google supplies an artifact for your Gemma 4 model and target runtime, and you need to reduce memory without giving up more quality than necessary. Google says its QAT checkpoints retain quality similar to bfloat16 while using less memory. Treat that as Google’s reported finding; verify quality on your own prompts and tasks.
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Choose PTQ when its format or runtime fits better
PTQ remains a reasonable choice if the runtime, quantization format, or model variant you need is not covered by an official QAT artifact—or if your own evaluation shows a PTQ option better meets your memory, speed, or quality target. The available sources do not compare every PTQ method with Gemma 4 QAT, so the label alone cannot settle the choice.
Decide on measured workload results, not the method name
For a fair comparison, use the same base model, representative tasks and prompts, context length, runtime version, and hardware. Evaluate the dimensions that matter to your application:
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- Task quality, including factuality, coding, or reasoning as applicable.
- Multimodal behavior, if your application uses image, audio, or other supported inputs.
- Latency and throughput under your expected concurrency.
- Total memory at the context length and output size you expect to serve.
This is a practical evaluation approach, not a claim that a benchmark was run here. Results can depend on the quantization method, runtime, hardware, and workload.
Match the checkpoint to your deployment
Google documents different QAT artifacts for different tools and use cases. The formats are not interchangeable by default; confirm the destination toolchain supports the checkpoint you select.
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| Deployment target | Documented QAT direction | What to know |
|---|---|---|
| Local inference with llama.cpp or LM Studio | Q4_0 GGUF | Google lists E2B, E4B, 12B, 26B-A4B, and 31B GGUF variants in its overview. |
| vLLM or SGLang server | W4A16 compressed tensors | Google’s overview lists E2B, E4B, 12B, and 31B. The vLLM recipe excludes 26B-A4B from 4-bit W4A16 because of excessive quality loss in that recipe, and suggests int8 per-channel weight-only quantization instead. Check current support and treat this as recipe-specific guidance. |
| Mobile or edge deployment | Mobile-optimized QAT | Google lists E2B and E4B. The mobile format uses static activations, channel-wise quantization, selected low-bit components, and KV-cache optimizations; it is specialized rather than a general-purpose interchangeable format. |
| Conversion to another format | Unquantized QAT checkpoint | Google documents these weights for custom downstream conversion or compilation. Whether conversion works depends on the destination toolchain. |
| Speculative decoding | QAT target model plus matching QAT assistant | Google’s model card says the assistant and target should use the same precision. See the official E2B Q4_0 GGUF model card. |
How much memory does QAT save?
Memory estimates are useful for narrowing options, but they are not complete device requirements. Google notes that base-weight estimates exclude software overhead and KV-cache memory. KV-cache use grows with prompt and generated tokens, so a model that fits at a short context may exceed available memory at a longer one or with more concurrent requests.
The vLLM recipe gives these estimated W4A16 memory figures for its documented deployment scenario. They are recipe estimates, not universal requirements for every runtime, device, or workload:
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| Gemma 4 variant | Recipe estimate before W4A16 | Recipe estimate with W4A16 |
|---|---|---|
| E2B | 9.8 GB | 7.3 GB |
| E4B | 15.2 GB | 9.8 GB |
| 12B | 22.8 GB | 8.3 GB |
| 31B | 59.0 GB | 19.8 GB |
The recipe’s estimates should not be treated as a promise that a particular device will run a model at a chosen context length or concurrency. Include weights, runtime overhead, KV cache, and workload when checking whether a deployment fits.
Mobile figures refer to specific configurations
In a June 5, 2026 article, Google says its mobile-specialized format reduced Gemma 4 E2B’s memory footprint to 1 GB. The same article separately says the text-only E2B configuration without Per-Layer Embeddings requires less than 1 GB. These are different configurations; neither figure establishes total memory use for every runtime, context, or input type. Google describes the mobile approach as using static activations, channel-wise quantization, targeted 2-bit layers, and embedding and KV-cache optimization. Read the Google announcement for its stated configuration details.
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A practical selection checklist
- Identify the model and runtime. Check whether Google provides a QAT artifact for the Gemma 4 variant and deployment tool you intend to use.
- Estimate complete memory needs. Account for model weights, software overhead, KV cache at your intended context length, output generation, and expected concurrency.
- Check variant-specific support. Do not assume every Gemma 4 model has the same 4-bit route; the vLLM recipe’s 26B-A4B exception is one documented example.
- Compare candidates on representative work. Hold the model, prompts, tasks, context, runtime, and hardware constant, then assess quality, latency, throughput, and total memory.
- Verify assistant-target precision if using speculative decoding. Google’s model card calls for matching precision between the QAT assistant and target.
Bottom line on Gemma 4 QAT vs. PTQ
Use an official QAT checkpoint as your first option when it matches your model and runtime, especially when memory is the constraint. Use PTQ when it offers the format or deployment support you need, or when your own workload evaluation favors it. Google reports an overall QAT quality advantage over standard PTQ baselines, but the available official material does not establish a universal winner or a numerical advantage for every task.
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