Which Model Would You Choose for Apple Silicon?

I was recently asked: which OS model would you choose for Apple’s M-series architecture?

Assuming “OS model” means an open-weight LLM, the best one to run and optimize on Apple Silicon is Qwen3-8B in 4-bit quantization. That is the model I chose for my Zig engine, zynfer.

Why it fits Apple Silicon well:

Approximately 5–6 GB of quantized weights, leaving room for the KV cache and workspace on a 16 GB Mac.
A dense transformer that cleanly exercises RMSNorm, RoPE, GQA, SwiGLU, KV caching, prefill, and decode.
Decode is strongly memory-bandwidth-bound, which makes it a useful target for Apple’s unified memory.
Mature MLX implementations exist, so you have a reference for correctness and performance. MLX officially supports Qwen3 on Apple Silicon: MLX-LM, supported models.

The development sequence I used:

Apple memory	Development model	Purpose
8–16 GB	Qwen3-0.6B / 1.7B	Kernel correctness and rapid debugging
16–24 GB	Qwen3-8B 4-bit	Primary optimization target
32–48 GB	Qwen3-14B 4-bit	More demanding production test
64 GB+	Qwen3-30B-A3B 4-bit	MoE and large-memory experimentation
96–192 GB	32B–70B-class 4-bit models	Max/Ultra bandwidth and capacity testing

For zynfer specifically:

Qwen3-0.6B as the correctness bootstrap.
Qwen3-8B 4-bit as the main Apple optimization target.
Qwen3-30B-A3B 4-bit later, to see whether MoE’s smaller active-parameter set improves generation efficiency.

I did not begin with Qwen3.5 or another hybrid attention/SSM architecture. Those models add recurrent-state and cache complexity before the backend boundary, Metal kernels, and a standard KV cache are proven. Current MLX reports also show prefix-cache complications for hybrid architectures. I started with a conventional dense Qwen3 transformer, and I will add hybrid models deliberately later.

Apple’s MLX notes that large-model memory wiring requires macOS 15 or later. See MLX-LM large-model guidance.

Qwen is a strong choice. It is not uniquely suited to Apple Silicon. MLX also supports Llama, Mistral, Gemma, Phi, DeepSeek-derived models, GPT-OSS, and many others. MLX supported-model list.

For a Zig/Metal engine, I ranked the families like this:

Family	Apple Silicon fit	Implementation difficulty	Best role
Llama 3.x	Excellent	Low	First architecture / reference
Qwen3	Excellent	Low–medium	Primary quality/performance target
Mistral	Excellent	Low–medium	Sliding-window and long-context testing
Gemma 3	Very good	Medium	Multimodal and memory-efficient testing
Phi	Very good	Low–medium	Small Macs and fast development
DeepSeek-R1 Distill	Excellent	Same as its base model	Reasoning-quality workload
GPT-OSS 20B	Promising	High	MoE and quantized-kernel target
Mixtral / Qwen MoE	Good with enough RAM	High	Sparse-MoE optimization research
Hybrid SSM models	Potentially excellent	Very high	Later research target

Llama: best first implementation

Llama 3.1 8B or Llama 3.2 3B is probably the cleanest family for establishing the engine.

Advantages:

Conventional decoder-only transformer.
RMSNorm, RoPE, GQA, SwiGLU, and ordinary KV caching.
Enormous ecosystem and many reference implementations.
Straightforward weight conversion and intermediate-output comparison.
MLX uses Llama 3.2 3B 4-bit as its default chat model. MLX-LM

Disadvantages:

Meta’s custom license is less permissive than Apache 2.0.
The available small Llama releases are no longer necessarily the strongest models at their sizes.
Fewer unusual architectural features for later research.

Verdict: best architectural bootstrap, even if Qwen becomes the main performance target.

Qwen: best overall project target

Qwen3 remained the preferred balance:

Strong small and medium models.
Dense and MoE variants within one family.
Apache 2.0 releases.
GQA and standard transformer operations map naturally onto the backend.
A clean progression from a tiny correctness model to a serious MoE experiment.
Strong coding, reasoning, and multilingual capability.

The progression:

Qwen3-0.6B
    ↓ correctness
Qwen3-4B or 8B
    ↓ dense Metal optimization
Qwen3-30B-A3B
    ↓ sparse MoE implementation

Verdict: the best primary family once the backend works.

Mistral: an excellent second dense architecture

Mistral 7B is attractive because it is close enough to Llama to reuse most kernels, while adding meaningful scheduling and cache differences.

Advantages:

Compact, well-understood architecture.
Strong reference support.
A useful sliding-window-attention test.
MLX directly supports Mistral 7B 4-bit. MLX server example

Disadvantages:

Older Mistral 7B models are no longer leading their size class.
Sliding-window attention complicates cache semantics without exercising entirely new compute kernels.

Verdict: an excellent test that the backend/model separation is real. If adding Mistral requires Metal-backend changes, the boundary is probably leaking model semantics.

Gemma 3: particularly interesting for Apple

Gemma 3 is a compelling Apple target because it provides 1B, 4B, 12B, and 27B sizes, multimodality, and long-context support. The 4B, 12B, and 27B variants advertise 128K input context. Gemma 3 model card

Advantages:

Good quality per parameter.
Convenient range of model sizes.
Quantization-aware releases can behave well at 4-bit.
Vision support creates a path toward a multimodal Apple backend.
A 12B 4-bit model is a good 16–24 GB Mac target.

Disadvantages:

Local/sliding attention patterns complicate KV-cache handling.
Multimodality introduces a vision encoder, preprocessing, and additional graph shapes.
Google’s Gemma terms are not as simple as Apache 2.0.
Less suitable than Llama or Qwen as the very first model.

Verdict: the best second-stage model if multimodal inference matters.

Phi: best for limited-memory Macs

Phi-3.5 Mini and related small models are useful on base M-series machines.

Advantages:

Small enough for rapid compile-test-debug cycles.
Leaves substantial memory for debugging buffers and intermediate captures.
Good for testing whether GPU dispatch overhead overwhelms useful work.
A useful benchmark for deciding when Accelerate CPU execution beats Metal.

Disadvantages:

Less representative of large-model memory-bandwidth behavior.
Optimizations that win on a small Phi model may not transfer to 8B–30B workloads.

Verdict: a valuable development fixture, not the final optimization target.

DeepSeek-R1 Distill: a workload, not a new backend

Models such as DeepSeek-R1-Distill-Qwen-7B use the Qwen architecture. Supporting the corresponding Qwen base generally gives you the distilled model automatically.

That is useful because you can test:

Longer reasoning generations.
KV-cache growth.
Sustained thermal behavior.
Long-run token latency.
Sampling and chat-template correctness.

Verdict: include it as a workload after Qwen support. Do not treat it as a separate kernel architecture.

GPT-OSS 20B: a fascinating Apple/MoE target

GPT-OSS 20B is a 21B-parameter mixture-of-experts model with approximately 3.6B active parameters per token, distributed under Apache 2.0. Official GPT-OSS 20B model card

Why it is interesting:

MoE reduces arithmetic per token relative to total parameter count.
Its original distribution uses MXFP4, which is a serious quantized-kernel target.
It exercises expert routing, top-k selection, grouped expert execution, and sparse weight access.
MLX has a native GPT-OSS implementation.

The Apple-specific catch matters: inactive weights still occupy unified memory, and sparse expert access can produce irregular bandwidth behavior. Low active parameters do not automatically mean low memory traffic. Efficient expert batching and fused dequantized matvec kernels become essential.

Verdict: probably the most intellectually interesting model after the dense backend is fast — and a poor first implementation.

The model roadmap

1. Llama 3.2 1B/3B
   Establish the simplest conventional architecture

2. Qwen3-0.6B
   Cross-family correctness and weight-loader validation

3. Qwen3-8B 4-bit
   Primary Metal, UMA, decode, and quantization target

4. Mistral 7B
   Validate sliding-window KV-cache behavior

5. Gemma 3 4B or 12B
   Add local attention and eventually vision

6. Qwen3-30B-A3B or GPT-OSS 20B
   Implement MoE routing and expert kernels

If I had to choose only one family, I would still choose Qwen. The engineering sequence can start with Llama, make Qwen3-8B the main performance target, and reserve GPT-OSS 20B for the advanced Apple-specific MoE work.