This is a submission for the Google I/O 2026 Writing Challenge "At Google I/O 2026, Google made a specific claim: Gemma 4 runs on consumer laptops without cloud dependency. They demoed offline coding on stage. Local AI on everyday hardware is finally practical, they said." GPU and high-bandwidth memory prices are not normal right now. AI companies are buying hardware at a scale that has genuinely disrupted the consumer market. A PC build suitable for local AI costs significantly more than it would have three or four years ago, if you can find the parts at all. If you bought your machine before the AI hardware gold rush, you have leverage most people do not. I bought my laptop four years ago. An RTX 3050 with 4GB VRAM is not a serious AI card by any current standard, but it is exactly the kind of hardware Google implied Gemma 4 would run on. For local inference to start feeling consistently comfortable beyond lightweight models, 16GB VRAM is where things become much less restrictive. I have 4GB. This is what that looks like. You install Ollama, pull the model, the weights load, the cursor blinks. The GPU appears busy. Fans are screaming. The model is loaded entirely in VRAM. And long-context inference still slows down much faster than most demos suggest. With Gemma 4 specifically, E2B loaded on my machine. E4B required closing everything else first to free RAM. Neither behaved the way the keynote implied. Real throughput was more nuanced than I expected. The slowdown was not catastrophic. That was the interesting part. E2B remained mostly inside GPU memory on this workload, which avoided the worst PCIe and shared-memory penalties. Small efficient models are now genuinely viable on consumer hardware. The problems start once context length, KV cache growth, and memory spillover begin compounding at the same time. nvidia-smi watch -n 1 nvidia-smi Every inference run has two phases. Prefill: the model reads your entire prompt in parallel. Compute-heavy, GPU handles it well. You generally do not feel this. Decode: the model generates each output token one at a time. This is memory-bound. Every token forces the GPU to reload model weights from memory again. The GPU finishes its math and waits. It is not slow. It is starving for bandwidth. It is why local inference feels slow even when Task Manager shows your GPU is busy. Check your own card before anything: nvidia-smi --query-gpu=name,memory.total --format=csv nvidia-smi -q | Select-String "Product Name", "Total", "Free", "Used" system_profiler SPHardwareDataType | grep -i bandwidth Even if your model fits in VRAM, that headroom disappears as your conversation grows. Every token the model has seen gets stored in the key-value cache. Without it, the model would reprocess the entire conversation on every generation step. The KV cache trades memory for speed. The tradeoff is it grows with every token. For Gemma 4 E2B, a moderately long conversation on a 4GB card will push you over the edge mid-generation. The model does not crash. It silently offloads to system RAM and your tokens per second falls off a cliff. Once inference spills heavily into system RAM, throughput collapses dramatically. OLLAMA_NUM_CTX=8192 ollama run gemma4:e2b ollama ps Most guides explain quantization as a way to make models smaller so they fit in VRAM. That undersells it. The real bottleneck is how fast the GPU can move weights from memory to compute units. Quantization reduces bytes per weight, so fewer bytes move per token generated. An INT4 model transfers 4 times less data per inference step than FP16, which translates almost directly to 4 times faster generation. Quantizing the KV cache separately is now supported in llama.cpp and is worth doing on constrained hardware: ./llama-cli \

-m gemma4-e2b-q4_k_m.gguf \
--n-gpu-layers 99 \ # push all layers to GPU
--cache-type-k q8_0 \ # quantize key cache
--cache-type-v q8_0 \ # quantize value cache
--ctx-size 4096 # keep context tight on 4GB cards
When VRAM is tight, --n-gpu-layers 20

on a 32-layer model sounds like a reasonable compromise. It is usually not. Partial off means some inference steps cross the PCIe bus, introducing high-latency transfers that stall the pipeline. The slowdown is not proportional to layers offloaded. Even a few CPU-side layers can significantly tank throughput. ./llama-cli \

-m gemma4-e2b-q4_k_m.gguf \
--n-gpu-layers 20 # partial offload = worst of both worlds

./llama-cli \

-m gemma4-e2b-q3_k_m.gguf \
--n-gpu-layers 99 # everything in VRAM, no PCIe stalls

This is where most people on Windows laptops get confused. While running Gemma 4 E4B, Task Manager showed the RTX 3050 at 0% GPU utilization. At the same time, nvidia-smi showed: Now the 11.6GB figure. This laptop has two GPUs: the RTX 3050 (GPU 1) and the AMD Radeon iGPU inside the Ryzen 7 6800H (GPU 0). The AMD iGPU has no dedicated VRAM. It borrows from system RAM dynamically. Windows adds them together: And here is system RAM during E4B inference: 13.2GB of 15.3GB used. 2.1GB available. Ollama is consuming roughly 4GB of system memory alongside the 3.5GB allocated in dedicated VRAM. The actual footprint for Gemma 4 E4B is 7 to 8GB total, split cleanly across two entirely different physical hardware pools running at wildly mismatched speeds. That split is exactly why generation feels slower than the model size alone would suggest. At the same time, Ollama alone was consuming nearly 8GB of system RAM:

#

The result is that local AI performance becomes a memory orchestration problem long before it becomes a compute problem. ./llama-bench -m gemma4-e2b-q4_k_m.gguf -p 512 -n 128 Get-Process ollama | Select-Object ProcessName,WorkingSet64 free -h nvidia-smi dmon -s mu sudo memory_pressure Gemma 4 E2B running locally on a 4GB VRAM laptop is not nothing. Four years ago that would not have been possible at all. The model quality for its size is genuinely impressive. But "runs on consumer laptops" and "runs well on consumer laptops" are different claims. The I/O keynote did not mention memory bandwidth, KV cache overflow, or the fact that the hardware shortage means GPUs with enough VRAM for comfortable inference are still expensive and unusually difficult to find. The model is the beginning of the problem. What happens after is a memory bandwidth race your hardware either wins or does not. Now you know which race you are in.