Apple M4 (32 GB)

Apple M4

Unified memory
32 GB
Memory bandwidth
120 GB/s
fp16 compute
8.1 TFLOPS
Class
Apple

Figures assume the 32 GB unified memory pool shared between CPU and GPU. "What runs on it" is judged at a 8,192-token context. Speeds are estimates, not measurements.

What runs on it

Model Sweet-spot quant Est. speed Community
DeepSeek-R1-Distill-Llama-8B deepseek-ai Q8_0 Runs fully on GPU @ 8K ctx 7–10 tok/s (estimate) no community data
DeepSeek-R1-Distill-Qwen-1.5B deepseek-ai Q8_0 Runs fully on GPU @ 8K ctx 34–45 tok/s (estimate) no community data
DeepSeek-R1-Distill-Qwen-14B deepseek-ai Q8_0 Runs fully on GPU @ 8K ctx 4–6 tok/s (estimate) no community data
DeepSeek-R1-Distill-Qwen-32B deepseek-ai IQ4_XS Runs fully on GPU @ 8K ctx 4–5 tok/s (estimate) no community data
DeepSeek-R1-Distill-Qwen-7B deepseek-ai Q8_0 Runs fully on GPU @ 8K ctx 8–11 tok/s (estimate) no community data
Devstral-Small-2-24B-Instruct-2512 mistralai Q4_K_M Runs fully on GPU @ 8K ctx 5–6 tok/s (estimate) no community data
GLM-4.7-Flash zai-org Q4_K_M Runs fully on GPU @ 8K ctx no community data
Kimi-VL-A3B-Instruct moonshotai Q8_0 Runs fully on GPU @ 8K ctx no community data
Llama-3.1-8B-Instruct meta-llama Q8_0 Runs fully on GPU @ 8K ctx 7–10 tok/s (estimate) no community data
Mistral-Small-3.2-24B-Instruct-2506 mistralai Q4_K_M Runs fully on GPU @ 8K ctx 5–6 tok/s (estimate) no community data
Phi-4-mini-instruct microsoft Q8_0 Runs fully on GPU @ 8K ctx 14–19 tok/s (estimate) no community data
Phi-4-reasoning microsoft Q8_0 Runs fully on GPU @ 8K ctx 4–6 tok/s (estimate) no community data
Qwen2.5-7B-Instruct Qwen Q8_0 Runs fully on GPU @ 8K ctx 8–11 tok/s (estimate) no community data
Qwen2.5-VL-32B-Instruct Qwen IQ4_XS Runs fully on GPU @ 8K ctx 4–5 tok/s (estimate) no community data
Qwen3-14B Qwen Q8_0 Runs fully on GPU @ 8K ctx 4–6 tok/s (estimate) no community data
Qwen3-30B-A3B-Instruct-2507 Qwen Q4_K_M Runs fully on GPU @ 8K ctx no community data
Qwen3-32B Qwen IQ4_XS Runs fully on GPU @ 8K ctx 4–5 tok/s (estimate) no community data
Qwen3-8B Qwen Q8_0 Runs fully on GPU @ 8K ctx 7–10 tok/s (estimate) no community data
Qwen3-Coder-30B-A3B-Instruct Qwen Q4_K_M Runs fully on GPU @ 8K ctx no community data
Qwen3-Embedding-0.6B Qwen Q8_0 Runs fully on GPU @ 8K ctx 46–61 tok/s (estimate) no community data
Qwen3-Embedding-4B Qwen Q4_K_M Runs fully on GPU @ 8K ctx 19–26 tok/s (estimate) no community data
Qwen3-Embedding-8B Qwen Q4_K_M Runs fully on GPU @ 8K ctx 12–16 tok/s (estimate) no community data
Qwen3-Omni-30B-A3B-Instruct Qwen Q4_K_M Runs fully on GPU @ 8K ctx no community data
Qwen3-Reranker-0.6B Qwen BF16 Runs fully on GPU @ 8K ctx 34–45 tok/s (estimate) no community data
Qwen3-Reranker-4B Qwen BF16 Runs fully on GPU @ 8K ctx 8–10 tok/s (estimate) no community data
Qwen3-Reranker-8B Qwen BF16 Runs fully on GPU @ 8K ctx 4–5 tok/s (estimate) no community data
Qwen3.6-27B Qwen Q4_K_M Runs fully on GPU @ 8K ctx 4–5 tok/s (estimate) no community data
Qwen3.6-35B-A3B Qwen IQ4_XS Runs fully on GPU @ 8K ctx no community data
SmolLM3-3B HuggingFaceTB BF16 Runs fully on GPU @ 8K ctx 11–14 tok/s (estimate) no community data
dots.ocr rednote-hilab BF16 Runs fully on GPU @ 8K ctx 11–15 tok/s (estimate) no community data
gemma-4-12B-it google Q8_0 Runs fully on GPU @ 8K ctx 5–7 tok/s (estimate) no community data
gemma-4-26B-A4B-it google Q4_K_M Runs fully on GPU @ 8K ctx no community data
gemma-4-31B-it google QAT-Q4_0 Runs fully on GPU @ 8K ctx 4–5 tok/s (estimate) no community data
gemma-4-E2B-it google Q8_0 Runs fully on GPU @ 8K ctx 14–19 tok/s (estimate) no community data
gemma-4-E4B-it google Q8_0 Runs fully on GPU @ 8K ctx 9–12 tok/s (estimate) no community data
gpt-oss-20b openai F16 Runs fully on GPU @ 8K ctx no community data
phi-4 microsoft Q8_0 Runs fully on GPU @ 8K ctx 4–6 tok/s (estimate) no community data

"Est. speed" is a modelled range labelled estimate (D8) for generation (decode) throughput. "Community" shows the median of approved user-submitted reports on this GPU class only where enough exist — never an estimate. "pp" is measured prompt-processing (ingestion) throughput from approved community reports; rows without a measurement show none.

What can I run on a Apple M4 (32 GB)?

On this GPU, 12 catalog models run fully on the GPU at an 8,192-token context. The most capable is Qwen3.6-35B-A3B at IQ4_XS (needs ~19.3 GiB). Pick a smaller model or a lower quant for more headroom.

Biggest model: Qwen3.6-35B-A3B at IQ4_XS

llama-server -m Qwen3.6-35B-A3B-UD-IQ4_XS.gguf -c 8192 -ngl 999

Derived from the fit engine at an 8,192-token context. See more answer packs.

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