You connected Gemma 4 to Antigravity, ran a few prompts, and then—nothing. The editor locks up, the chat panel goes silent, and buried in the status bar you see something like GGML_METAL_MAX_BUFFERS exceeded or out of memory. This is a common wall people hit when first running Gemma 4 locally, and it's almost always fixable without giving up on local models entirely. As an indie developer running my whole toolchain on one machine, I ran into it repeatedly trying to push the 27B model on a 16 GB MacBook—freezing the editor more than once—before working out the order of fixes that actually holds up.
Why Gemma 4 Triggers Out of Memory Errors
Gemma 4 comes in several sizes, and the memory requirements jump dramatically between them:
- Gemma 4 2B (fp16): ~4 GB RAM
- Gemma 4 9B (fp16): ~18 GB RAM
- Gemma 4 27B (fp16): ~54 GB RAM
Most developers who want "the full Gemma 4 experience" reach for the 27B model. The problem is that running it at full fp16 precision requires over 54 GB of memory—far beyond what most development machines have available.
When Antigravity connects to Ollama in the background, this failure typically looks like one of these messages:
Error: model 'gemma4:27b' failed to load: GGML_METAL_MAX_BUFFERS exceeded
llama_new_context_with_model: failed to allocate memory
error: out of memory
Sometimes the error doesn't surface visibly at all—the chat just hangs indefinitely. If Gemma 4 stops responding after the first few messages, memory pressure is usually the culprit.
Step 1: Check Current Memory Usage
Before changing settings, it helps to know exactly how much memory you're working with.
# macOS: check memory pressure
vm_stat | grep "Pages"
# Check what Ollama is currently using
ps aux | grep ollama | grep -v grep
# See which models are loaded
ollama psAs a rough rule of thumb: loading a model requires about 1.2–1.5× its file size in available memory. A Q4_K_M quantized 27B model is around 17 GB, so you need roughly 20–25 GB free just for the model—before Antigravity, your browser, or anything else.
On Apple Silicon Macs, keep in mind that memory is unified between CPU and GPU. Layers offloaded via num_gpu_layers draw from the same physical pool, so the intuition that "pushing work to the GPU frees up main memory" doesn't hold here—budget your free space as one combined total.
Fix 1: Switch to a Quantized Model
This is the most impactful change you can make. Instead of running fp16 (full precision), use a quantized variant. Ollama makes this straightforward:
# Q4_K_M: good balance of quality and memory usage
ollama pull gemma4:27b-it-q4_K_M
# Smaller footprint, slightly lower quality
ollama pull gemma4:27b-it-q2_KThe Q4_K_M quantization reduces memory usage to roughly 17 GB for the 27B model—still large, but workable on a 32 GB machine. For everyday coding tasks in Antigravity (completions, explanations, refactoring suggestions), the quality difference between fp16 and Q4_K_M is rarely noticeable in practice.
Fix 2: Reduce the Context Window Size
The context window is a silent memory consumer. By default, Ollama often sets num_ctx at 32768 tokens or higher, which can consume several additional gigabytes of GPU/RAM.
You can create a custom Modelfile to lock this down:
# Create a Modelfile with reduced context
FROM gemma4:27b-it-q4_K_M
PARAMETER num_ctx 8192
PARAMETER num_gpu_layers 20
# Build a new model from the Modelfile
ollama create gemma4-8k -f Modelfile
# Then point Antigravity's AI provider to 'gemma4-8k'The num_gpu_layers parameter controls how many model layers get offloaded to the GPU (Metal on Apple Silicon). If memory is tight, try reducing this to 10–20.
Fix 3: Use the 9B Model Instead
If you don't specifically need 27B capabilities, the 9B model is worth trying first. It requires about 6–7 GB with Q4_K_M quantization—manageable on a 16 GB machine with a few apps closed.
ollama pull gemma4:9b-it-q4_K_MFor code completion and inline chat in Antigravity, the 9B model performs well in most scenarios. The speed improvement alone (faster completions = better flow state) often outweighs any quality difference for day-to-day development work.
For setting up Gemma 4 with Ollama and Antigravity from scratch, see Connecting Gemma 4 to Antigravity via Ollama. For a broader look at integrating Gemma 4 into a local development workflow, Gemma 4 Local LLM Development Workflow Guide covers the full setup.
Fix 4: Free Up Memory by Stopping Unused Processes
Antigravity + Ollama + a browser with many tabs can easily consume 12+ GB before the model even loads. Closing browser tabs, quitting background apps, or restarting your machine to clear memory leaks can make a real difference.
Ollama keeps models resident in memory after loading. To free that memory when switching models:
# Check what's currently loaded
ollama ps
# Unload a specific model (Ollama v0.1.24+)
ollama stop gemma4:27bVerify the Fix
After making changes, restart Antigravity and test Ollama directly from the terminal:
# Confirm Ollama responds correctly before testing in Antigravity
curl http://localhost:11434/api/generate \
-d '{
"model": "gemma4:27b-it-q4_K_M",
"prompt": "Respond with one sentence confirming you are working.",
"stream": false
}'
# Expected output: {"model":"gemma4:...","response":"I am working correctly...","done":true,...}If this returns a valid response, Ollama is healthy. If Antigravity still doesn't respond after this, the issue is in the connection settings (URL, port, or model name) rather than memory.
For general Antigravity performance issues not related to Gemma 4 specifically, Fixing High CPU and Memory Usage in Antigravity covers the broader picture.
Quick Reference
When you hit an OOM error, work through this checklist:
- Switch to a quantized model (Q4_K_M) — solves most cases immediately
- Reduce
num_ctxto 8192 in a Modelfile - Drop from 27B to 9B if memory is still tight
- Stop unused processes and unload other Ollama models
Starting with the quantized model swap resolves this for most setups. Give that a try first before adjusting other settings.