LLM bora kwa Mac ya GB 36.

Modeli huria 38 kati ya 73 zinaenda vizuri ndani ya GB 36. Kwa chaguo-msingi GPU inaweza kutumia takriban GB 27 kati yake. Kasi hutegemea chipu: kuanzia 150 GB/s kwenye M3 Pro hadi 460 GB/s kwenye M5 Max.

Yenye akili zaidi inayoenda vizuriQwen3.8 27B4-bit · 15.2 GB · 3–24 tok/sKubwa zaidi inayoenda vizuriQwen3.6 35B A3B4-bit · 19.7 GB · 40–216 tok/sKwa kuandika codeQwen3 Coder 30B A3B Instruct6-bit · 24.8 GB · 25–136 tok/s

Mac zenye GB 36

Chipu 4 kuanzia M3 hadi M5 huja na GB 36. Fungua yoyote kuona orodha na kasi zake.

Zinaenda vizuri 40

Inatosha na kubakiza nafasi ya context, kwa tokeni 10 au zaidi kwa sekunde kwenye M5 Max.

ModeliToleoKumbukumbuM3 ProM5 Max
Qwen3.8 27B27B · Akili 33.74-bit15.2 GB3–514–24
Qwen3.6 35B A3B35B · 3B zinazotumika · Akili 18.24-bit19.7 GB40–71122–216
Muse Glimmer 30B29.6B · Akili 17.54-bit16.7 GB3–512–22
Gemma 4 31B30.7B · Akili 14.74-bit17.3 GB3–512–21
Nemotron 3.5 Lightning 30B A3B30B · 3B zinazotumika · Akili 12.96-bit24.4 GB28–5086–153
Granite 4.2 30B30B · Akili 12.74-bit16.9 GB3–512–22
MiniCPM5 2B2.52B · Akili 12.58-bit2.7 GB25–4477–136
Qwen3.5 9B9B · Akili 11.28-bit9.6 GB13–2422–38
Granite 4.2 8B8B · Akili 11.18-bit8.5 GB10–1824–43
Granite 4.2 3B3B · Akili 9.18-bit3.2 GB21–3865–115
gpt-oss-20b21B · 3.6B zinazotumika · Akili 94.25-bit asili13.8 GB36–63109–194
Nemotron 3 Nano 30B A3B30B · 3.5B zinazotumika · Akili 8.96-bit24.4 GB24–4374–131
Gemma 4 E4B8B · Akili 8.98-bit8.5 GB14–2543–77
Ministral 3 8B8.9B · Akili 5.58-bit9.5 GB14–2422–39
Nemotron 3 Nano 4B3.97B · Akili 4.88-bit4.2 GB16–2949–88
Qwen3 Coder 30B A3B Instruct30.5B · 3.3B zinazotumika6-bit24.8 GB25–4477–136
GLM-4.7 Flash30B · 3B zinazotumika6-bit24.4 GB28–5086–153
Qwen3.6 27B27B4-bit15.2 GB3–514–24
Gemma 4 26B A4B25.2B · 3.8B zinazotumika6-bit20.5 GB22–3967–119
Devstral Small 2 (24B)24B6-bit19.5 GB3–611–19
Magistral Small 1.2 (2509)24B6-bit19.5 GB3–611–19
Mistral Small 3.2 (24B)24B6-bit19.5 GB3–611–19
LFM2.5 24B A2B24B · 2B zinazotumika6-bit19.5 GB42–75129–230
Phi-4 Reasoning Vision 15B15B8-bit15.9 GB4–813–23
Phi-4 (14B)14B8-bit14.9 GB5–814–25
Gemma 4 12B Unified12B8-bit12.7 GB10–1816–29
Qwen3 8B8.2B8-bit8.7 GB10–1824–42
Llama 3.1 8B Instruct8B8-bit8.5 GB10–1824–43
Gemma 4 E2B5.1B8-bit5.4 GB28–5086–153
Qwen3.5 4B4B8-bit4.3 GB16–2848–86
Phi-4 mini instruct3.8B8-bit4 GB17–3052–92
Llama 3.2 3B Instruct3.21B8-bit3.4 GB20–3561–108
SmolLM3 3B3B8-bit3.2 GB21–3865–115
LFM2.5 2.6B2.69B8-bit2.9 GB23–4171–127
Qwen3.5 2B2B8-bit2.1 GB32–5799–175
Llama 3.2 1B Instruct1.23B8-bit1.3 GB52–92159–283
Qwen3.5 0.8B0.8B8-bit0.9 GB75–133230–409
Qwen3 0.6B0.6B8-bit0.6 GB113–200345–613
Nemotron 3.5 Bolt 74B A9B74B · 9B zinazotumika · Akili 21.42-bit ubora wa chini23.1 GB24–4374–131
Qwen3 Coder Next80B · 3B zinazotumika · Akili 9.22-bit ubora wa chini25 GB75–133230–409

Zinaenda, lakini polepole 1

Inatosha, lakini chini ya tokeni 10 kwa sekunde: sawa kwa kazi za kundi, polepole kwa mazungumzo.

ModeliToleoKumbukumbuM3 ProM5 Max
Llama 3.3 70B Instruct70B2-bit ubora wa chini21.9 GB3–59–17

Kubwa mno 32

Inahitaji kumbukumbu zaidi ya GB 36, hata kwa biti 2.

Maswali

LLM ipi ni bora kwa Mac yenye GB 36?
Miongoni mwa modeli zenye Intelligence Index iliyochapishwa, Qwen3.8 27B ina alama ya juu zaidi kati ya zile zinazoenda vizuri ndani ya GB 36 (4-bit). Kubwa zaidi inayoenda vizuri ni Qwen3.6 35B A3B. Kwa code: Qwen3 Coder 30B A3B Instruct.
Je, GB 36 zinatosha kwa AI ya ndani kwenye Mac?
Modeli huria 38 kati ya 73 zilizo hapa zinaenda vizuri ndani yake kwa biti 4 au zaidi. Kwa chaguo-msingi macOS huiruhusu GPU kutumia takriban GB 27 kati yake; modeli na context yake lazima zitoshe humo.
Je, Mac ya GB 36 inaweza kuendesha gpt-oss-20b?
Ndiyo, 4.25-bit asili, kwa takriban tokeni 36–63 kwa sekunde kwenye M3 Pro na 109–194 kwenye M5 Max.

Ukubwa mwingine: 8 GB · 16 GB · 18 GB · 24 GB · 32 GB · 36 GB · 48 GB · 64 GB · 96 GB · 128 GB · 192 GB · 256 GB · 512 GB

Ziendeshe kwa MLX Harness (bure, open source) au mlx-lm ya Apple ndani ya Terminal: hatua kwa hatua.

Jinsi inavyokokotolewa

Kumbukumbu: vigezo vya modeli kwa biti 8, 6, 4, 3 au 2 kwa kila weight pamoja na group scales za MLX (nusu biti kwa kila weight), au muundo wake asili, pamoja na nafasi ya context juu yake. Kwa chaguo-msingi macOS huiruhusu GPU kutumia takriban 75% ya unified memory. Kasi: bandwidth ya kumbukumbu ikigawanywa kwa baiti zinazosomwa kwa kila tokeni ndiyo kikomo cha juu kwa mazungumzo moja; katika benchmarks za umma za llama.cpp kwenye Apple Silicon uendeshaji halisi hufikia takriban 45% hadi 80% yake, na huo ndio upana unaoonyeshwa. Haya ni makadirio, si vipimo: pima Mac yako mwenyewe kwenye MLX Bench. Takwimu za modeli ni kutoka kwa kadi rasmi ya kila modeli, takwimu za chipu ni kutoka Tech Specs za Apple. Imekaguliwa 2026-10-19. Data ileile ya mlx.app/compare.

LLM bora za ndani kwa Mac ya GB 36 (M3 hadi M5)