Best Open-Weight LLMs for Mac in 2026: RAM, Ollama Tags & Picks

As of August 15, 2026. “Which open-weight LLM is best?” is the wrong first question for a Mac. The useful constraints are unified memory, model package, context length, input modality and runtime. A model can appear small enough on paper and still trigger memory pressure or CPU offloading after a long prompt.

The comparison is practical rather than a universal ranking: it asks which model is a sensible first test on a given Mac and what limitations appear after that.

If the model choice is part of buying a new machine, the separate MacBook for AI development guide 2026 covers the hardware decision before you test a model.

Choosing by unified memory

Mac unified memory	Sensible starting point	Do not promise
8 GB	Gemma 4 E2B or a smaller tag	large context or comfortable multitasking
16 GB	Gemma 4 E2B/E4B, smaller Qwen tags	large agent runs are memory-sensitive
24 GB	Gemma 4 12B or Qwen3.6 27B with headroom	26B/35B with long context and many apps
32 GB	Qwen3.6 27B or 35B, Gemma 4 26B	package size is not free RAM
48 GB	Gemma 4 31B or Qwen3.6 35B with more room	256K context can still be too much
64 GB+	evaluate larger local variants deliberately	Llama 4 Scout remains a specialist case

These are starting points, not performance guarantees. For a purchase or production decision, test the exact model with your own prompts, context and tools.

What the current Ollama pages show

The following values are Ollama package sizes, not peak measurements from ai-on-mac.com:

Family/tag	Ollama listing	Context in Ollama	Input
qwen3.6:27b	17 GB	256K	text, image
qwen3.6:35b	24 GB	256K	text, image
qwen3.6:27b-mlx	20 GB	256K	text, image
qwen3.6:35b-mlx	22 GB	256K	text, image
gemma4:e2b	7.2 GB	128K	text, image
gemma4:e4b	9.6 GB	128K	text, image
gemma4:12b	7.6 GB	256K	text, image
gemma4:26b	18 GB	256K	text, image
gemma4:31b	20 GB	256K	text, image
llama4:16x17b	67 GB	10M	text, image

The pages were retrieved on August 15, 2026; new tags and repackaging can change these values. In particular, the current Ollama listing shows gemma4:e4b at 9.6 GB rather than the older 7.9 GB value.

Package size is not memory use

A local run needs more than the file shown in the model library:

• model weights and runtime,
• KV cache for the selected context,
• image or audio encoders,
• macOS and background processes,
• browser, editor and other applications,
• temporary buffers for long or parallel tasks.

Apple Silicon shares a unified memory pool between CPU and GPU. An 18 GB package may therefore start on a 24 GB Mac but leave almost no room for 256K context or other applications. A clean start is not proof of comfortable use.

Qwen3.6: coding and agents

Ollama currently lists Qwen3.6 in 27B and 35B variants plus MLX tags. The model cards describe improvements for coding agents, repository tasks, multimodal inputs and preserving thinking context across historical messages.

# 27B: smaller text/image starting point
ollama pull qwen3.6:27b
ollama run qwen3.6:27b

# 35B: larger variant, more memory headroom required
ollama pull qwen3.6:35b
ollama run qwen3.6:35b

The current Ollama pages also list qwen3.6:27b-mlx and qwen3.6:35b-mlx with text and image input.

Qwen publishes extensive benchmark tables covering coding, agents, vision and video. The model cards also document differing harnesses, timeouts, context lengths, prompts and internal datasets. Treat the scores as source material, not as a direct Mac ranking. The first practical question is whether context and tool calls run without unacceptable offloading on your machine.

Gemma 4: more sizes and explicit on-device targets

Google describes Gemma 4 as a multimodal family with dense and MoE variants:

• E2B and E4B for smaller devices,
• 12B as the Unified model,
• 26B A4B as an MoE model,
• 31B as a dense model.

Google’s model card lists native audio on E2B, E4B and 12B. The current Ollama entries primarily show text and image input for local tags, so audio is not automatically available in every Ollama/client combination.

# lighter starting point
ollama pull gemma4:e4b
ollama run gemma4:e4b

# 12B: more capability at a moderate package size
ollama pull gemma4:12b
ollama run gemma4:12b

# 26B MoE: plan memory deliberately
ollama pull gemma4:26b
ollama run gemma4:26b

On a 24 GB Mac, gemma4:12b is the more sensible first test. Ollama lists gemma4:26b at about 18 GB, leaving little headroom for context, images and other applications. Google’s model card lists the 26B A4B model at 25.2B total and 3.8B active parameters. That makes it more efficient than 31B dense, but not memory-free.

Llama 4 Scout: large context, not a normal Mac install

Ollama lists llama4:16x17b at 67 GB and a 10M context. Meta’s model card describes Scout as a 109B MoE model with 17B active parameters. The combination of a 67 GB package and 10M context is impractical for ordinary local Macs:

# only for very large-memory or server experiments
ollama pull llama4:16x17b

Even 64 GB of unified memory does not mean that a 67 GB package plus runtime, macOS and KV cache will fit comfortably. Ollama lists the 128x17B variant at 245 GB and 1M context. For everyday Mac work, Qwen3.6 or Gemma 4 are more realistic candidates.

Context length: the setting that distorts comparisons

Ollama’s current documentation gives defaults by VRAM:

• below 24 GiB: 4K context,
• 24–48 GiB: 32K,
• 48 GiB or more: 256K.

Apple Silicon uses shared memory, so these thresholds should not be read as an exact Mac RAM guarantee. Ollama recommends at least 64K for coding, agents and web search, while also warning that larger contexts need more memory.

Check the actual allocation after loading a model:

OLLAMA_CONTEXT_LENGTH=64000 ollama serve
ollama ps

ollama ps shows the model, size, processor split and context. If CPU offloading or memory pressure appears, reduce context and close parallel apps before concluding that the model itself is too slow.

Why local speed numbers require a test

Tokens per second depend on model tag, quantization, backend, context, prompt, temperature, Metal/MLX path and background load. Community reports and vendor numbers are useful for initial orientation, but are not reproducible enough for a purchase or production decision.

A fair local benchmark records at least:

1. exact Mac model and unified memory;
2. macOS and Ollama versions;
3. model tag and quantization;
4. context length and prompt size;
5. prompt and output tokens;
6. prefill and decode speed;
7. CPU/GPU offload and memory pressure.

Practical choice

For open-weight LLMs on a Mac, package size, context and free unified memory matter together. Gemma 4 E2B/E4B targets smaller machines, Gemma 4 12B is a sensible 24 GB starting point, Qwen3.6 27B/35B targets larger coding and agent setups, and Llama 4 Scout remains a specialist for very large memory environments despite its 10M context.

The current sources also show why a static ranking ages quickly: Ollama tags, package sizes and input modalities change. Check the exact tag, start with a bounded context and measure your own workflow before turning a model name into a buying recommendation.

Sources

• Ollama: Qwen3.6 — current tags, package sizes, context and input modalities.
• Qwen3.6 27B Model Card and 35B-A3B Model Card — architecture, benchmarks and methodology notes.
• Qwen: Qwen3.6-27B and Qwen3.6-35B-A3B — official release framing and open-weight availability.
• Ollama: Gemma 4 — current local tags and sizes.
• Google Gemma 4 Model Card — modalities, sizes, context and benchmarks.
• Ollama: Llama 4 — Scout/Maverick tags, package sizes and context.
• Meta: Llama 4 — official Scout/Maverick model framing.
• Ollama: Context length — context defaults and ollama ps.