Best Open-Source LLMs for Apple Silicon Macs in 2026

Quick Answer

Google’s gemma-4-26B-A4B-it is the top pick as of September 2026, with 11,073,424 monthly downloads resolving the popularity tiebreak among candidates with no public benchmark yet [9]. In order, the ranking is gemma-4-26B-A4B-it (Google), Qwen3.5-9B (Qwen), gemma-4-31B-it (Google), Qwen3.8-27B (Qwen), NVIDIA-Nemotron-3-Nano-4B-BF16 (NVIDIA), and GLM-4.7-Flash (zai-org) [9][6].

Key Takeaways

• Google’s gemma-4-26B-A4B-it ranks first through the popularity tiebreak among eligible models with no public benchmark yet, with 11,073,424 downloads over the last 30 days; that position does not establish a quality or speed advantage.[9]
• Qwen’s Qwen3.5-9B ranks second, with 19.3 GB of BF16 weights; Google’s gemma-4-31B-it ranks third, with 62.5 GB of BF16 weights.[6][7]
• Qwen’s Qwen3.8-27B ranks fourth, with 55.6 GB of BF16 weights and an Apache-2.0 license.[5]
• NVIDIA’s NVIDIA-Nemotron-3-Nano-4B-BF16 ranks fifth, with 7.9 GB of BF16 weights and the nvidia-nemotron-open-model-license.[1]
• zai-org’s GLM-4.7-Flash ranks sixth, with 62.4 GB of BF16 weights and an MIT license.[3]
• Treat those BF16 weight sizes as published storage figures, not verified 4-bit memory requirements: measured Apple Silicon speed, quantized memory use and comparative quality remain unestablished for this ranking.[1][3][5][6][7][9]

How do these models compare on published specifications and public benchmarks?

Which open-weight LLMs should you consider for an Apple Silicon Mac?

1. gemma-4-26B-A4B-it

gemma-4-26B-A4B-it by Google ranks first through the popularity tiebreak among eligible candidates with no public benchmark yet, recording 11,073,424 downloads over the last 30 days as of September 25, 2026.[9] Download activity determines its placement here; it does not establish quality or Apple Silicon performance. Treat the ranking as a starting point for evaluation, with local speed and output quality still to verify.

Google lists 25.8 billion parameters, a context window of 256K tokens, and BF16 weights totaling 51.6 GB.[9] The model was first published on Hugging Face on March 11, 2026, under the Apache-2.0 license.[9] The published weight size describes the BF16 release; it does not establish the unified memory needed for a quantized deployment. A supported quantized build and its runtime memory requirements are needed before recommending a particular Mac configuration.

The practical use here is local evaluation against your own engineering tasks before committing to hardware. Check answers against known results and measure memory consumption and generation speed in your intended runtime. The caveat is that measured Apple Silicon throughput and quantized memory requirements remain unestablished, so neither a specific hardware fit nor a speed advantage can be promised. The advertised context window also does not establish what will run comfortably on your Mac.

2. Qwen3.5-9B

Qwen3.5-9B by Qwen ranks second under the release-window and popularity-tiebreak rules, with 9,781,405 downloads over the reporting month, behind Google’s gemma-4-26B-A4B-it with 11,073,424.[6][9] Qwen3.5-9B has no public benchmark yet, so its position is a shortlist order rather than a demonstrated quality advantage. Downloads resolve the unscored tie; they do not establish how well the model runs on an Apple Silicon Mac.

Published on February 27, 2026, Qwen3.5-9B has 9.7 billion parameters, a context length of 256K tokens, and 19.3 GB of BF16 weights.[6] The Apache-2.0 license makes it a candidate for local projects whose licensing requirements allow that license.[6] A practical use is evaluating your own prompts and workflows before committing to a larger deployment. Treat the advertised context length as a model specification, not a verified operating target for your Mac.

Hardware sizing remains the caveat: the published BF16 weight size does not establish a complete unified-memory requirement.[6] Quantized memory consumption, Apple Silicon generation speed, and runtime compatibility remain unverified here. Choose a specific runtime and model conversion, then check their memory requirements against your Mac before downloading. A defensible recommendation for a particular Mac configuration needs those measurements.

3. gemma-4-31B-it

gemma-4-31B-it by Google ranks third through the download tiebreak among eligible, unscored releases, behind Google’s gemma-4-26B-A4B-it and Qwen’s Qwen3.5-9B.[9][6][7] Its 9,263,106 downloads over the preceding 30 days establish that position; popularity does not establish a quality advantage.[7] Benchmark status: no public benchmark yet. The ranking therefore gives you an evaluation order, without establishing which model performs better on your Mac.

Published specifications list 31.3 billion parameters, a 256K-token context window and 62.5 GB of BF16 weights under the Apache-2.0 license.[7] The model first appeared on Hugging Face on March 11, 2026.[7] Consider it for local evaluation when the license and advertised context window match your requirements. Those specifications provide a reason to investigate the model, but cannot establish its suitability for coding, reasoning or another particular workload.

Hardware planning remains the caveat: the published 62.5 GB BF16 weight size is not a measured runtime memory requirement.[7] No measured 4-bit memory footprint or Apple Silicon generation speed is established here, so a specific Mac configuration cannot be recommended confidently. Before committing hardware, verify runtime compatibility, quantized memory use and performance at your intended context length. Treat the advertised context capacity as a specification to validate in your deployment.

4. Qwen3.8-27B

Qwen3.8-27B by Qwen ranks fourth in this shortlist under the download tiebreak, behind Google’s gemma-4-26B-A4B-it, Qwen’s Qwen3.5-9B and Google’s gemma-4-31B-it.[5][9][6][7] Its public benchmark status is “no public benchmark yet.” With 6,579,319 downloads over the preceding 30 days as of September 25, 2026, its position reflects adoption among the eligible, unscored candidates; that position does not establish superior quality or Apple Silicon performance.[5]

Qwen first published the model on August 5, 2026, with 27.8 billion parameters, a 256K-token context window and 55.6 GB of BF16 weights.[5] The Apache-2.0 license makes it worth considering for local experimentation where licensing requirements matter.[5] A practical evaluation would use your own prompts and documents to check answer quality and context handling before committing to a deployment. No task-specific quality advantage is established for this model.

Hardware planning remains the caveat. The published 55.6 GB figure describes BF16 weights, not measured Mac memory consumption.[5] A verified quantized memory footprint, measured generation speed and confirmed Apple Silicon runtime support are not available. Consequently, a specific Mac configuration or comfortable context setting cannot be recommended confidently. Treat Qwen3.8-27B as an evaluation candidate whose local suitability still needs validation.

5. NVIDIA-Nemotron-3-Nano-4B-BF16

NVIDIA-Nemotron-3-Nano-4B-BF16 by NVIDIA ranks fifth because the eligible candidates have no public benchmark yet for this comparison, leaving download popularity to break the tie.[1][5][6][7][9] NVIDIA reports 4,222,312 downloads over the last 30 days as of September 25, 2026.[1] Download counts determine its position here; they do not establish reasoning quality or performance on Apple Silicon. Treat the placement as a shortlist position rather than a measured performance recommendation.

The model has 4B parameters, a 256K-token context length and 7.9 GB of BF16 weights.[1] NVIDIA first published it on March 7, 2026, under the nvidia-nemotron-open-model-license.[1] Local reasoning experiments are a sensible intended use, consistent with the reasoning focus of NVIDIA’s accompanying Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs paper.[2] Evaluate it on representative tasks before choosing it for an engineering workflow.

For hardware planning, the published 7.9 GB weight size describes BF16 weights, not total application memory.[1] Allow room for the operating system, inference runtime and context, and verify runtime compatibility before downloading. The practical caveat is unverified Apple Silicon fit: measured quantized memory consumption and generation speed are unavailable for this comparison. A specific Mac memory configuration therefore cannot be recommended confidently; validate memory use and responsiveness at your intended context length.

6. GLM-4.7-Flash

GLM-4.7-Flash by zai-org ranks sixth in the general-purpose shortlist, with 1,816,463 downloads over the reporting month providing the popularity tiebreak.[3] Its benchmark status is “no public benchmark yet”; the position does not establish a quality advantage. Download counts settle the ordering among unscored candidates, while practical suitability still needs evaluation on your intended Mac and workload.

Published on Hugging Face on January 19, 2026, GLM-4.7-Flash has 31.2 billion parameters, a context length of 198K tokens, and BF16 weights totaling 62.4 GB.[3] The model uses the MIT license.[3] For hardware planning, treat that weight size as a starting point rather than a verified unified-memory requirement. A specific Mac configuration cannot be recommended confidently without validated quantized memory consumption and runtime support.

Consider GLM-4.7-Flash for local coding and reasoning evaluation, consistent with the application areas described in zai-org’s accompanying GLM technical report.[4] Test representative prompts before committing hardware to the deployment. The practical caveat is that measured Apple Silicon generation speed and memory use at 4-bit precision are not established by its published BF16 size.[3] Confirm both for the exact runtime and quantized build you intend to use; neither download popularity nor advertised context length establishes responsiveness.

Why does Google's gemma-4-26B-A4B-it take first place through the download tiebreak when every pick has no public benchmark yet ?

Google’s gemma-4-26B-A4B-it takes first place through the download tiebreak because every eligible pick has “no public benchmark yet,” and its 11,073,424 downloads put it ahead of the other candidates in the same reporting window.[9][6][7][5][1][12][10][3][16][14]

The ranking applies eligibility before comparison. Google published gemma-4-26B-A4B-it on March 11, 2026, placing it within the required release window.[9] Recency establishes eligibility; the rule does not order eligible models by release date. With every candidate unscored, benchmark comparison leaves the field tied, and downloads resolve that tie.

The download figures make the ordering explicit. Over the preceding 30 days, as reported on September 25, 2026, Qwen’s Qwen3.5-9B recorded 9,781,405 downloads, while Google’s gemma-4-31B-it recorded 9,263,106.[6][7] Both trail gemma-4-26B-A4B-it’s 11,073,424.[9] Moving a smaller model above those entries because of weight size would introduce a different ranking criterion.

For an Apple Silicon buyer, that placement has a limited meaning: popularity settles the unscored comparison. Google lists 25.8B parameters and 51.6 GB of BF16 weights for gemma-4-26B-A4B-it.[9] Those specifications describe the published model; quantized memory requirements, measured Mac generation speed, and comparative answer quality remain unestablished here. The download tiebreak supports its position in this list, while practical hardware suitability still requires separate validation.

What memory and speed measurements are needed to judge local performance on a Mac?

Judge local performance on a Mac by measuring peak unified-memory use, time to first token, prompt-processing throughput, and sustained generation throughput under a repeatable workload.

Record the exact Mac chip, installed unified memory, macOS version, inference software, model revision, and quantization format. Keep the prompt, input length, output length, and concurrency consistent across comparisons. Document whether model loading is included in the timing, and report cold-start and warmed-up runs separately.

Measure memory after loading and during both prompt processing and generation. Record the configured context length, actual tokens used, peak process memory, system memory pressure, and swap activity. Published weight sizes provide context: Qwen’s Qwen3.5-9B has BF16 weights of 19.3 GB,[6] while Google’s gemma-4-26B-A4B-it has BF16 weights of 51.6 GB.[9] Neither figure establishes the memory needed by a quantized build during inference.

Report prompt processing and generation separately in tokens per second, alongside time to first token in seconds. Repeat each workload and report variation; include a sustained run to check whether throughput changes over time. Document power settings and competing applications.

A useful comparison pairs those measurements with the exact quantized artifact and representative prompts. Without measured memory and timing results for that configuration, leave Mac performance unverified rather than converting published weight sizes into a fit or speed claim.

Which licenses apply to these model weights?

The model weights use Apache-2.0, MIT, nvidia-nemotron-open-model-license, or nvidia-license, depending on the model.[1][3][5][14] Check the license attached to your chosen model before adopting its weights for a project.

Qwen’s Qwen3.8-27B and Qwen3.5-9B use Apache-2.0.[5][6] Google’s gemma-4-31B-it and gemma-4-26B-A4B-it also use Apache-2.0, as does deepseek-ai’s DeepSeek-OCR-2.[7][9][16] For those models, record Apache-2.0 alongside the exact model name in your project’s dependency documentation.[5][6][7][9][16]

The MIT group comprises zai-org’s GLM-4.7-Flash, baidu’s Unlimited-OCR, and deepseek-ai’s DeepSeek-OCR.[3][10][12] Pay attention to the DeepSeek model names: DeepSeek-OCR lists MIT, while DeepSeek-OCR-2 lists Apache-2.0.[12][16] Avoid copying a license entry from a related model without checking the exact repository.

NVIDIA’s NVIDIA-Nemotron-3-Nano-4B-BF16 lists nvidia-nemotron-open-model-license.[1] NVIDIA’s LocateAnything-3B instead lists nvidia-license.[14] Keep those identifiers distinct, and read the corresponding terms before deciding whether either model suits your intended use.

For local deployment, record the model repository and its license together. Before redistributing weights, packaging them with an application, or using a modified version, check the applicable license text for permissions, conditions, and notices. Treat “open-weight” as a reason to inspect the terms, rather than as a substitute for that review.

Frequently Asked Questions

Which model takes the first position in this comparison?

Google’s gemma-4-26B-A4B-it takes the first position through the popularity tiebreak among eligible, unscored candidates, with 11,073,424 downloads over the preceding 30 days as of September 25, 2026.[9] Benchmark status: no public benchmark yet. Download counts determine the ordering here; they do not establish quality or Apple Silicon performance. The published BF16 weights occupy 51.6 GB.[9]

How much Mac memory do I need for quantized weights?

A supported memory recommendation requires a measured quantized build and runtime configuration. NVIDIA’s NVIDIA-Nemotron-3-Nano-4B-BF16 has published BF16 weights of 7.9 GB, while Qwen’s Qwen3.5-9B has BF16 weights of 19.3 GB.[1][6] Neither figure establishes quantized runtime memory. Before choosing a download, check its actual weight size and measure total memory use at your intended context length.

How fast will these models run on my Apple Silicon Mac?

Measured Apple Silicon generation speeds are unavailable for this comparison. Qwen’s Qwen3.8-27B lists 55.6 GB of BF16 weights, and zai-org’s GLM-4.7-Flash lists 62.4 GB; those specifications do not establish token throughput.[5][3] For a useful comparison, measure prompt processing and generation separately on your target Mac, keeping the runtime, quantization, prompt, and output length consistent.

Which model should I choose for coding and reasoning?

A coding or reasoning winner cannot be established here. Google’s gemma-4-31B-it, Qwen’s Qwen3.8-27B, and zai-org’s GLM-4.7-Flash each carry the status “no public benchmark yet” in this comparison.[7][5][3] Treat any model-card benchmark as self-reported. Evaluate candidates against representative tasks from your own work before using download counts to break a remaining tie.

Are all of these models released under the same open-source license?

License terms differ. Google’s gemma-4-26B-A4B-it and Qwen’s Qwen3.5-9B list Apache-2.0, while zai-org’s GLM-4.7-Flash lists MIT.[9][6][3] NVIDIA’s NVIDIA-Nemotron-3-Nano-4B-BF16 lists nvidia-nemotron-open-model-license.[1] Check the license attached to the exact model you plan to use, particularly before redistribution or commercial deployment. An article’s open-source label should not replace that review.

Should I choose an OCR or grounding model for everyday chat?

Choose according to the task you need to perform. Baidu’s Unlimited-OCR, DeepSeek’s DeepSeek-OCR, and DeepSeek’s DeepSeek-OCR-2 address OCR or document-oriented visual processing.[11][13][17] NVIDIA’s LocateAnything-3B addresses vision-language grounding.[15] Those specializations do not establish general chat or coding quality. Evaluate document reading and object localization separately from conversational assistance rather than treating every candidate as interchangeable.

Sources

1. nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 model card (Hugging Face) — 2026-09-25
2. Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs — 2025-11-20
3. zai-org/GLM-4.7-Flash model card (Hugging Face) — 2026-09-25
4. GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models — 2025-08-08
5. Qwen/Qwen3.8-27B model card (Hugging Face) — 2026-09-25
6. Qwen/Qwen3.5-9B model card (Hugging Face) — 2026-09-25
7. google/gemma-4-31B-it model card (Hugging Face) — 2026-09-25
8. Gemma 4 Technical Report — 2026-07-02
9. google/gemma-4-26B-A4B-it model card (Hugging Face) — 2026-09-25
10. baidu/Unlimited-OCR model card (Hugging Face) — 2026-09-25
11. Unlimited OCR Works — 2026-06-22
12. deepseek-ai/DeepSeek-OCR model card (Hugging Face) — 2026-09-25
13. DeepSeek-OCR: Contexts Optical Compression — 2025-10-21
14. nvidia/LocateAnything-3B model card (Hugging Face) — 2026-09-25
15. LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding — 2026-05-26
16. deepseek-ai/DeepSeek-OCR-2 model card (Hugging Face) — 2026-09-25
17. DeepSeek-OCR 2: Visual Causal Flow — 2026-01-28
18. meta-llama/Llama-3.2-11B-Vision-Instruct model card (Hugging Face) — 2024-09-18
19. meta-llama/Llama-4-Scout-17B-16E-Instruct model card (Hugging Face) — 2025-04-02
20. deepseek-ai/DeepSeek-V4.1-Flash model card (Hugging Face) — 2026-09-10
21. meta-llama/Llama-3.2-11B-Vision model card (Hugging Face) — 2024-09-18
22. meta-llama/Llama-4-Maverick-17B-128E model card (Hugging Face) — 2025-04-02
23. moonshotai/Kimi-K2.6 model card (Hugging Face) — 2026-04-14