Ollama Update: Gemma 4 Nearly 90% Faster on Apple Silicon, a New Scheduler, and a Web Search API

Ollama, the default way most of us run open models locally, shipped a dense two weeks: Gemma 4 now generates tokens nearly 90 percent faster on Apple Silicon, a rebuilt scheduler squeezes real speed out of GPUs you already own, and a new web search API gives local models live internet access. Two of the three make your current hardware faster for free. The third deserves one honest caveat. Here is the rundown.

What's new, with numbers

• Gemma 4 on Apple Silicon (v0.31.1, June 30): nearly 90 percent faster token generation on average, measured on the Aider polyglot coding-agent benchmark with Gemma 4 12B (nvfp4) on an M5 Max, per the official post. Gemma 4 ships a small draft model that proposes several tokens ahead; the main model verifies them in one pass, and Ollama now auto-tunes the draft length at runtime. On by default, and it does not change model output. A new MLX matmul kernel (2x to 2.5x faster on M5 Max) helps other models too.
• New model scheduler: Ollama now measures exactly how much memory a model needs instead of estimating. Official numbers: gemma3:12b at 128K context on an RTX 4090 jumps from 52.02 to 85.54 tokens per second (64 percent faster) because all 49 layers now fit on the GPU, and image-input prompt processing on a dual-4090 rig goes roughly 10x, from 127.84 to 1,380.24 tokens per second (details and benchmarks). Fewer out-of-memory crashes and honest memory reporting come along for the ride.
• Web search API: a hosted endpoint at ollama.com with a free tier for individuals, callable from curl, Python (ollama.web_search()), or JavaScript, built for feeding live results to local models as an agent tool (announcement). Ollama has not published exact free-tier limits, just "generous."

Smaller but welcome, in v0.31.2 (July 6): flash attention enabled on Pascal-era NVIDIA GPUs (compute capability 6.x), and a fix for model loading on paths with non-UTF-8 characters.

Why it matters for local builders

The scheduler is the sleeper here. A 64 percent generation speedup at long context on a 4090, from a software update, is the kind of gain we usually pay for in silicon. If you run agent loops against a local Qwen 3.6 or point OpenCode at an Ollama endpoint, you get that for free by upgrading. And multi-token prediction landing in the mainstream local runtime matters beyond Gemma: it is the same speculative-decoding idea the big MoE models like GLM-5.2 ship with, now working out of the box on a Mac.

One honest caveat

Web search is not local. Your queries go to Ollama's servers, authenticated with an API key, and the feature sits next to a paid cloud tier (Free, $20 Pro, $100 Max). Parts of r/LocalLLaMA and Hacker News read this as cloud creep in a tool people chose precisely because it kept everything on-device; that is community sentiment, not an accusation, but the underlying point stands. Know which of your calls stay local and which do not, and skip the API key entirely if air-gapped is the point of your setup.

Try it in 10 minutes

# Update Ollama (or grab the installer from ollama.com/download)
curl -fsSL https://ollama.com/install.sh | sh

# Feel the MTP speedup on an Apple Silicon Mac
ollama run gemma4:12b

# Optional: web search (key from ollama.com/settings/keys; this call leaves your machine)
curl https://ollama.com/api/web_search \
  --header "Authorization: Bearer $OLLAMA_API_KEY" \
  -d '{"query": "LongCat-2.0 benchmarks"}'

Sources and further reading

• Ollama blog: faster Gemma 4 with multi-token prediction
• Ollama blog: new model scheduling
• Ollama blog: web search API
• Release notes on GitHub
• Ollama cloud pricing tiers

Tested on: not independently tested; no Apple Silicon or RTX 4090 bench available to us this week. All performance figures are Ollama's own published benchmarks (Aider polyglot on M5 Max; RTX 4090 and dual-4090 scheduler runs), linked above.
Date checked: 2026-07-09