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Gemma 4 arrived in April 2026 with benchmark jumps that look too large to be real. They are real, and the architecture explains why. This guide breaks down the PLE, MoE, and thinking mode decisions that drive the gains, the hardware constraints that govern deployment, and which variant fits your specific workload.
The most telling number in the Gemma 4 release isn't the parameter count or the Arena AI ranking. It's the agentic tool use score.
On the τ2-bench retail benchmark, which tests a model's ability to plan across multiple steps, call tools in the right sequence, and recover from errors, Google's official model card documents that Gemma 3 27B scored 6.6%. Gemma 4 31B scores 86.4%. The 26B MoE model reaches 85.5%. A model at 6.6% cannot be relied upon for agentic workflows in any production context. At 86.4%, it enters the range where autonomous task execution becomes viable.
The math and reasoning gains follow the same pattern. The same official model card shows Gemma 4 31B scoring 89.2% on AIME 2026 mathematics, against Gemma 3 27B's 20.8%. On competitive programming, the Codeforces ELO climbed from 110 to 2,150. An ELO of 110 means essentially unable to solve problems designed for competitive programmers. An ELO of 2,150 means Candidate Master level, placing the model in the top few hundred globally. On LiveCodeBench v6, the score moved from 29.1% to 80.0%. On GPQA Diamond, a graduate-level science reasoning benchmark, it went from 42.4% to 84.3%.
As of April 2, 2026, the Google DeepMind model page records the 31B model at an Arena AI ELO of 1452 and the 26B MoE at 1441, placing them third and sixth globally among open models. Both positions represent substantial improvement over Gemma 3 27B's 1365.
The benchmark scores cited throughout this article reflect instruction-tuned models with thinking mode enabled. Performance without thinking mode will be lower on math and reasoning tasks.
These results need a structural explanation, not just a parameter count comparison. Gemma 4 31B has approximately 30.7 billion parameters, a modest increase over Gemma 3 27B's roughly 27 billion. The gains are not coming from scale.
The underlying change is architectural: Gemma 4 adds native thinking mode, which lets the model generate a chain of reasoning before committing to an answer, and native function calling, which is trained into the weights rather than patched through prompt engineering. Both capabilities are present across the entire Gemma 4 family. Their combined effect on agentic task performance, measured in a single generation, is not an improvement in degree. It likely reflects what happens when native function calling and built-in thinking mode act together as architectural features rather than as post-hoc additions, though the exact contribution of each cannot be isolated from public benchmarks alone. This conclusion only becomes visible when the benchmark data is read against the architectural change documentation rather than any single source's performance claim.
Gemma 4 spans four models, but two distinct architectural approaches serve two distinct deployment tiers. Each approach was built to maximize intelligence-per-parameter for different hardware constraints.
The E2B and E4B models carry an "E" prefix that stands for "effective parameters." The E2B has 2.3 billion effective parameters, but 5.1 billion total when embedding tables are included. That distinction matters.
Standard transformer architectures encode each input token as a single vector at the beginning of the network. That vector carries all token-identity information through every subsequent layer. Per-Layer Embeddings, or PLE, takes a different approach: each decoder layer receives its own small, dedicated conditioning vector for every token. These embedding tables are large in raw bytes but are accessed only as fast lookup tables, not as active computation. The result is that each layer of the network gets token-specific context tuned precisely for its position in the processing stack, giving a 2.3B active parameter model representational depth that would otherwise require a much larger network.
This architecture is why the E2B can run in under 1.5GB of memory on optimized mobile deployments via LiteRT-LM with 2-bit and 4-bit quantization, while still producing outputs that outperform the previous generation's 27B flagship on most benchmarks. The hardware footprint is small. The intelligence-per-byte is not.
The 26B A4B model is named for its active parameter count. Google's model card specifies the full configuration: 25.2B total parameters, 3.8B active during inference, with a pool of 128 total experts, 8 active per token, plus one shared expert that fires on every token regardless of routing. The "A4B" in the name means 4 billion active parameters.
Why 128 experts rather than the 8 or 16 that earlier MoE models typically used? MindStudio's architecture analysis documents that Google uses auxiliary loss terms during training to enforce even distribution across all 128 experts, preventing a few dominant experts from absorbing most tokens. With 128 narrow specialists, each expert develops deep competence in a smaller domain. The router can match tokens to experts with more precision. The utilization rate is 6.25%: only 1 in 16 experts fires for any given token. The compute cost per inference pass is therefore roughly equivalent to running a 4B dense model, while the knowledge capacity is that of a 26B network.
Gemma's MoE architecture is not a drop-in replacement for the MLP blocks in its layers — it adds MoE layers alongside them and sums their outputs, which is structurally different from how DeepSeek and Qwen implementations are built. As the DEV Community technical analysis documents, that design choice trades some compute efficiency for architectural simplicity, and it has implications for both inference characteristics and fine-tuning compatibility.
The 26B MoE activates only 3.8B parameters per token; yet it requires loading all 26B into memory before a single inference can begin, because the router must have instant access to all 128 experts to make its selection. The compute savings are real and measurable: inference runs at approximately 4B-class speed. But the memory savings do not exist. Every expert must be resident in VRAM, available for routing, before the first token can be processed. This separation between inference compute and inference memory is the most commonly misunderstood aspect of MoE deployment, and it directly shapes the hardware decisions covered in the next section.
The attention mechanism across all Gemma 4 variants uses a hybrid design, interleaving local sliding-window attention with full global attention at intervals, with the final layer always global. Sliding-window attention in the edge models spans 512 tokens per layer; the workstation models use 1024-token windows. This allows the network to run efficiently on shorter contexts while retaining the full long-range awareness needed for complex multi-turn reasoning.
Global attention layers also apply shared Keys and Values across heads, reducing the memory consumed by the KV cache. This optimization matters directly for context length, a topic the hardware section addresses in detail.
The thinking mode present across all four model variants allows any Gemma 4 model to generate extended internal reasoning, up to 4,000 or more tokens, before producing a final answer. This is not fine-tuning or prompting. It is a trained capability built into the architecture, triggered by including a control token at the start of the system prompt. It is the primary driver of the mathematics and reasoning benchmark gains.
The benchmark numbers reflect real capabilities. The hardware required to access those capabilities on local hardware is a separate and often more restrictive question.
The practical memory requirements break down as follows. Unsloth's documentation specifies approximately 18GB VRAM for the 26B A4B at 4-bit quantization and approximately 20GB for the 31B dense model at the same precision. At 8-bit precision, both figures rise roughly 40 to 50 percent, pushing the 31B past what most consumer GPUs hold without quantization. For the edge models, requirements are substantially lower: the E2B can run in under 1.5GB on optimized mobile deployments via LiteRT-LM, as confirmed in the Google Developers Blog.
ModelVRAM at Q4VRAM at Q8ContextE2B~3–4 GB~6 GB128KE4B~5–6 GB~9 GB128K26B A4B~18 GB~28 GB256K31B Dense~20 GB~34 GB256K
Sources: Unsloth documentation, Google Developers documentation. Figures represent model weights only and do not include KV cache overhead.
The table above covers model weights. It does not cover the context.
Avenchat's hardware analysis documents that a 24GB GPU reaches its VRAM ceiling at roughly 45,000 tokens of context on the 31B model, that full 256K context requires at least 40GB of VRAM, and that the KV cache alone consumes approximately 22GB on top of model weights at that context length. Reaching the advertised maximum therefore requires a 48GB workstation GPU, a dual-GPU configuration, or Apple Silicon with 48 to 64GB of unified memory.
For the 26B A4B at full 256K context, the memory demand is comparable. The MoE model requires all 26B parameters in VRAM plus the KV cache at whatever context length is active. At 128K context, the 26B A4B achieves over 1,000 prompt processing tokens per second on an RTX 3090, fast enough for practical agentic workflows. The 31B dense model runs at approximately 30 to 34 tokens per second on the same hardware, compared to 64 to 119 tokens per second for the MoE. Both speed figures reflect the fundamental difference between dense and sparse inference: the dense model must compute across all 30.7 billion parameters on every token, while the MoE routes each token through only 3.8 billion of its 26 billion.
The 4-bit quantization tradeoff is modest at approximately 2 to 5 percent benchmark degradation relative to full precision, which is acceptable for most production use cases. CPU-only inference is possible via llama.cpp but runs at 5 to 10 tokens per second, usable for testing but impractical for regular workflows. Apple Silicon's unified memory architecture, which shares a single memory pool between CPU and GPU, makes it particularly well-suited for running larger models where VRAM is typically the constraint on discrete GPU setups.
Early community reports documented inference latency issues with the 26B MoE at launch. Verifying current performance against the latest Ollama build is worth doing before making hardware decisions.
A 24GB GPU runs the 31B model. A 24GB GPU also runs into the KV cache ceiling at roughly 45,000 tokens, well below the advertised 256K maximum. For teams planning to use Gemma 4 for RAG pipelines, long-document analysis, or whole-codebase context, the hardware sizing question is not "does my GPU have enough VRAM for the model?" It is "does my GPU have enough VRAM for the model plus the KV cache at the context length my workflow actually requires?" Most deployment planning that relies on the advertised context window without accounting for the cache will encounter memory pressure in production.
Prior generations of open models typically treated multimodal capability as an add-on. Audio required an external ASR pipeline. Vision encoders were bolted onto text backbones. Function calling depended on prompt engineering and hoping the model cooperated. Gemma 4 integrates all of these at the architecture level, and the resulting efficiency gains reflect that structural difference.
The Let's Data Science post-launch analysis documents that the E2B and E4B edge models achieve 4x faster inference and 60% lower battery consumption than their Gemma 3 equivalents, not from parameter reduction alone but from architectural integration that allows the runtime to schedule compute more efficiently across multimodal inputs.
For edge deployment specifically, the performance figures from the Google Developers Blog are concrete. On a Raspberry Pi 5 running CPU inference, the model achieves 133 prefill tokens per second and 7.6 decode tokens per second. On a Qualcomm Dragonwing IQ8 with NPU acceleration, those numbers jump to 3,700 prefill tokens per second and 31 decode tokens per second. These figures are for the edge models specifically, and they demonstrate that on-device inference is viable for real-time workflows on modern mobile chipsets.
The official model card documents that audio input is limited to the E2B and E4B variants; the 26B and 31B models process text, image, and video but do not accept audio input. This is the architecture's most counterintuitive characteristic: the smallest, most edge-optimized models are also the most multimodal. Llama 4 has no models under 109B total parameters, ruling it out for edge deployment entirely. Qwen 3.5's small models lack audio support. Gemma 4 is currently the only open-weight family that covers the full spectrum from phone to workstation under one Apache 2.0 license, with audio available at the edge tier and image and video processing available across all sizes.
For video, the model processes sequences of frames up to 60 seconds at one frame per second, across all four variants. For images, a configurable visual token budget allows the model to trade off detail for speed: lower budgets (70 to 280 tokens) suit classification, captioning, and video frame processing, while higher budgets (560 to 1120) preserve fine detail for OCR and document parsing tasks.
Deployment tooling at launch covered most major inference frameworks: Ollama, LM Studio, Hugging Face Transformers, LiteRT-LM, vLLM, llama.cpp, and MLX all supported Gemma 4 on day one. Fine-tuning had more friction. PEFT could not handle a new layer type introduced in the vision encoder at launch, and a novel training field required custom workarounds for teams attempting to fine-tune immediately. Both issues were tracked in HuggingFace repository issues. Teams planning domain-specific fine-tuning should verify current library compatibility before assuming day-one readiness applies.
The benchmark gap between Gemma 4 31B and its nearest open-weight competitors is measured in single-digit percentage points. The licensing gap between Apache 2.0 and Llama 4's 700-million MAU cap is measured in legal departments. This asymmetry is the real competitive picture: benchmark distances narrow with each model cycle, while structural licensing constraints and coverage gaps in the small model tier do not close until competitors ship entirely new architectures.
Lushbinary's comparison analysis documents that Qwen 3.5 27B leads Gemma 4 31B on MMLU Pro by approximately one point (86.1% vs. 85.2%) and on GPQA Diamond by slightly more (85.5% vs. 84.3%). Gemma 4 31B leads on AIME 2026 and Codeforces ELO. The same analysis documents Llama 4 Scout's context window at 10 million tokens against Gemma 4's 256K, a gap that matters for whole-codebase or long-document workloads. GLM-5, Qwen 3.5 397B, and Kimi K2.5 all carry benchmark leads over Gemma 4 at larger scales. For frontier multi-step reasoning at maximum scale, they win.
Artificial Analysis documents that Gemma 4 31B produces 39 million output tokens to complete the Intelligence Index benchmark, versus 98 million for Qwen 3.5 27B at a score just 3 points higher. That 2.5x efficiency gap compounds across millions of API calls and translates directly to inference cost reduction in production environments. Token efficiency matters as much as benchmark rank: a model scoring 3 points lower on a benchmark but requiring 2.5x fewer output tokens is often the better production choice.
Llama 4's structure creates a coverage gap that benchmarks don't capture. The family's smallest model starts at 109B total parameters, making it server-only by default. Any deployment scenario requiring edge devices, phones, laptops, or hardware under roughly 50GB of memory falls outside Llama 4's range entirely. Gemma 4 covers that range from the E2B up. VentureBeat's analysis documents that the E4B edge model scores 42.5% on AIME 2026, outperforming Gemma 3 27B's 20.8% from a model that runs on a laptop. That result has no equivalent in the current Llama 4 or Qwen 3.5 small model lineups.
Llama 4's community license also restricts applications that could scale beyond 700 million monthly active users and requires attribution in product interfaces. Apache 2.0 imposes neither constraint. For enterprises building products where scale is a future possibility or where legal review of license terms consumes time and risk budget, the clean permissive license is a substantive operational advantage. This suggests that the Apache 2.0 distinction may be a more durable competitive advantage than the benchmark lead, which competitors are actively narrowing.
The four Gemma 4 models map to four distinct hardware tiers with minimal overlap. The deployment decision is primarily a hardware inventory question. Start there.
The edge models are the right choice when the deployment target is a mobile device, a Raspberry Pi, an IoT device, or any scenario where connectivity is unreliable and data must never leave the device. The E4B is the recommended starting point for most laptop deployments where portability and audio support both matter. At approximately 5 to 6GB of VRAM at 4-bit quantization, it runs on any modern laptop with a discrete GPU and on Apple Silicon unified memory configurations starting around 16GB. The E2B suits scenarios where memory is the tightest constraint: under 4GB at Q4, it is the only frontier-class open model that fits on smartphone NPUs at the time of writing.
Both models support audio input natively, making them the only option in the Gemma 4 family for speech recognition, audio transcription, and voice-driven agentic workflows.
The 26B MoE model is the practical production choice for teams with 16GB or more of VRAM. It delivers benchmark quality close to the 31B flagship while running at roughly 4B-class inference speed. Prompt processing at 128K context exceeds 1,000 tokens per second on consumer hardware, which enables responsive agentic workflows. The trade-off is context: at full 256K context, the KV cache consumes memory at the same rate as the 31B, so hardware sizing still needs to account for the cache, not just the model weights. The ~11 tokens per second text generation speed at Q4 on an RTX 4090 is slower than the 31B Dense's ~25 tokens per second, because MoE routing overhead partially offsets the active-parameter savings during generation.
The 31B model is the quality ceiling in the family and the better platform for fine-tuning, since the dense architecture is more compatible with parameter-efficient fine-tuning methods. At approximately 20GB at 4-bit quantization, it fits on 24GB consumer GPUs for inference at moderate context lengths. It is slower than the MoE variant during generation but does not carry the routing overhead that can create latency variability in the MoE at launch. For teams building local AI coding assistants or IDE integrations, the 31B dense model's combination of code benchmark performance and fine-tuning compatibility makes it the strongest foundation. If your team is also evaluating cloud-based AI coding tools, our comparison of Cursor 3, Claude Code, and Codex covers the architectural differences that determine whether local or cloud deployment is the right fit for your coding workflow.
Gemma 4 does not compete with Qwen 3.5 397B or DeepSeek V3.2 on frontier multi-step reasoning; for the hardest 5% of queries in a production stack, cloud or larger open models remain the better answer. Production architectures that route fast and cheap tasks to E4B, complex reasoning to 26B MoE, and only the most demanding queries to a cloud or larger open model can realistically reduce inference costs by 60 to 80% while preserving output quality for the work that matters most.
Fine-tuning is supported but required more patience at launch than inference did. The official model card notes that fine-tuning memory requirements are substantially higher than inference requirements, with exact needs depending on batch size, sequence length, and whether full-precision or parameter-efficient methods are used.
At launch, PEFT could not handle Gemma4ClippableLinear, a new layer type in the vision encoder, and a novel training field required workarounds for mixed-modality datasets. Both issues had active HuggingFace repository threads as of launch week. Before attempting fine-tuning, verify current library compatibility with the latest versions of Hugging Face Transformers and PEFT. Unsloth's documentation covers LoRA and QLoRA fine-tuning workflows for Gemma 4 and is updated as upstream library support stabilizes.
Gemma 4 and Gemini share a research foundation. Google built Gemma 4 from the same architecture and training methodology as Gemini 3, but packaged it as open weights for local deployment. Gemini runs on Google's infrastructure; you send queries to an API. Gemma runs on hardware you control; data never leaves your environment.
The tradeoff is capability ceiling versus data sovereignty. Gemini 3 Pro and the commercial frontier models deliver capabilities that Gemma 4 at 31B does not fully replicate, particularly at large-context and highly complex multi-step reasoning tasks. But for organizations under data residency requirements, building products where input data is sensitive, or running high-volume workloads where per-token API costs add up, Gemma 4 eliminates the cloud dependency entirely. The Google DeepMind model page confirms the Apache 2.0 license for Gemma 4, meaning there are no usage restrictions, no per-token charges, and no data passing to Google's servers once the weights are downloaded.
It depends on the use case and the maturity of the tooling you need. For prototyping and forward-compatibility work, the AICore Developer Preview is available today, as the Google Developers Blog confirms, enabling on-device experimentation with E2B and E4B directly in Android Studio. The ML Kit GenAI Prompt API supports production in-app deployment via LiteRT-LM. Google's stated timeline is that Gemini Nano 4 will ship on flagship Android devices later in 2026, with the AICore Developer Preview designed for forward-compatibility with that release.
For teams shipping production AI features into existing Android apps, the E4B model via LiteRT-LM is the most production-ready path today. Fine-tuning for domain-specific tasks carries the same compatibility caveats noted above: verify PEFT and Transformers library support before committing to a fine-tuned deployment timeline.
For interactive use, the 26B MoE is often faster on prompt processing but slower on token generation. Its inference cost is close to a 4B model for computation purposes, while generation runs at approximately 11 tokens per second on consumer hardware at Q4, compared to the 31B Dense's approximately 25 tokens per second. The benchmark gap between the two is small on most tasks, with the MoE's 85.5% on τ2-bench and 88.3% on AIME 2026 sitting only a few points below the 31B's scores.
The 31B Dense has advantages in two specific scenarios. First, it is the better fine-tuning platform because the dense architecture is straightforwardly compatible with standard parameter-efficient methods. Second, early community reports noted inference latency issues specific to the MoE model at launch that the Dense variant did not exhibit. If you need immediate stability without monitoring Ollama release notes, the 31B is the lower-friction choice. If throughput for prompt-heavy workflows matters more than raw quality or fine-tuning plans, the 26B MoE is the stronger production candidate once tooling stabilizes.