M5 Max LLM benchmark
On the M5 Max, measured single-user chat decode speeds are: ~35.5B models at 88.3 tok/s (Q6_K, ollama; Measured, 1 result, source run). Short-chat workload, local runtimes, dense models only; quantization, backend and settings vary between submissions, so check each row in the tables below.
Specs vendor specs, not measured
- Unified memory (max configuration)
- 128 GB
- Memory bandwidth
- 460 GB/s (18-core CPU / 32-core GPU, 36 GB); 614 GB/s (18-core CPU / 40-core GPU, 48 / 64 / 128 GB)
- Launch date
- 2026-03-03
Sources: support.apple.com, apple.com. Vendor-published figures, accessed 2026-10-14.
Fastest single result in these submissions (any model size)
88.3 decode tok/s
qwen3.6-agent-q6k-ctx128k (35.5B) via ollama (Q6_K). Workload: chat-short. Reported hardware: M5 Max (40-core GPU) + 48GB unified. see full run
qwen3.6-agent-q6k-ctx128k
| Workload | Model size | Reported hardware | Backend | Quant | decode tok/s | prefill tok/s | TTFT | Run |
|---|---|---|---|---|---|---|---|---|
| chat-short | 35.5B | M5 Max (40-core GPU) + 48GB unified | ollama@0.32.1 | Q6_K | 88.29tok/s | 16.20tok/s | 6,915ms | r_udwgba7udqu |
Community folklore on M5 Max
15 unverified claims extracted from Reddit/HN comments. Lower trust than signed runs above; every row links to the source.
- communityconfidence 70%
24.79tok/s — Gemma 3 27B on M5 Max via lm-studio
our signed data: M5 Max · Gemma 3 27B
“W|**+45W** (Thermal throttling risk)| |**Time to First Token (Prefill)**|89.83s|24.35s|**\~3.7x Faster**| |**Generation Speed**|23.16 tok/s|24.79 tok/s|**+1.63 tok/s** (Marginal)| |**Total Time**|847.87s|787.85s|**\~1 minute faster** overall| |**Prompt Tokens**|19,761|19,761|Same…”
- communityconfidence 70%
23.16tok/s — Gemma 3 27B on M5 Max via lm-studio
our signed data: M5 Max · Gemma 3 27B
“|< 70W|< 115W|**+45W** (Thermal throttling risk)| |**Time to First Token (Prefill)**|89.83s|24.35s|**\~3.7x Faster**| |**Generation Speed**|23.16 tok/s|24.79 tok/s|**+1.63 tok/s** (Marginal)| |**Total Time**|847.87s|787.85s|**\~1 minute faster** overall| |**Prompt Tokens**|19,761…”
- communityconfidence 60%
31.60tok/s — Qwen 2.5 72B on M5 Max via mlx
our signed data: M5 Max · Qwen 2.5 72B
“oretical maximum bandwidth utilization. # 2. MLX is Dramatically Faster for Qwen 3.5 * **llama.cpp**: 16.5 tok/s (Q6\_K, 21GB) * **MLX**: 31.6 tok/s (4bit, 16GB) * **Delta**: MLX is **92% faster** (1.9x speedup) This confirms the community reports that llama.cpp has a known pe…”
- communityconfidence 60%
16.50tok/s — Qwen 2.5 72B on M5 Max via llama.cpp
our signed data: M5 Max · Qwen 2.5 72B
“onsistently achieves \~73-75% of theoretical maximum bandwidth utilization. # 2. MLX is Dramatically Faster for Qwen 3.5 * **llama.cpp**: 16.5 tok/s (Q6\_K, 21GB) * **MLX**: 31.6 tok/s (4bit, 16GB) * **Delta**: MLX is **92% faster** (1.9x speedup) This confirms the community r…”
- communityconfidence 60%
1.63tok/s — Gemma 3 27B on M5 Max via lm-studio
our signed data: M5 Max · Gemma 3 27B
“rmal throttling risk)| |**Time to First Token (Prefill)**|89.83s|24.35s|**\~3.7x Faster**| |**Generation Speed**|23.16 tok/s|24.79 tok/s|**+1.63 tok/s** (Marginal)| |**Total Time**|847.87s|787.85s|**\~1 minute faster** overall| |**Prompt Tokens**|19,761|19,761|Same context worklo…”
- communityconfidence 55%
26.00tok/s — on M5 Max via mlx bf16
our signed data: M5 Max
“Results **Qwen3.5-9B bf16** |Gen length|DFlash|Baseline|Speedup| |:-|:-|:-|:-| |1024 tokens|85 tok/s|26 tok/s|3.3x| |2048 tokens|80 tok/s|26 tok/s|3.1x| **Qwen3.5-4B bf16** |Gen length|DFlash|Baseline|Speedup| |:-|:-|:-|:-| |1024 tokens|109 tok/s|41 tok/s|2.7x| |2048 tokens|1…”
- communityconfidence 55%
80.00tok/s — on M5 Max via mlx bf16
our signed data: M5 Max
“CUDA. # Results **Qwen3.5-9B bf16** |Gen length|DFlash|Baseline|Speedup| |:-|:-|:-|:-| |1024 tokens|85 tok/s|26 tok/s|3.3x| |2048 tokens|80 tok/s|26 tok/s|3.1x| **Qwen3.5-4B bf16** |Gen length|DFlash|Baseline|Speedup| |:-|:-|:-|:-| |1024 tokens|109 tok/s|41 tok/s|2.7x| |2048…”
- communityconfidence 55%
85.00tok/s — on M5 Max via mlx bf16
our signed data: M5 Max
“). **Setup:** M5 Max, 64GB, MLX, no CUDA. # Results **Qwen3.5-9B bf16** |Gen length|DFlash|Baseline|Speedup| |:-|:-|:-|:-| |1024 tokens|85 tok/s|26 tok/s|3.3x| |2048 tokens|80 tok/s|26 tok/s|3.1x| **Qwen3.5-4B bf16** |Gen length|DFlash|Baseline|Speedup| |:-|:-|:-|:-| |1024 …”
- communityconfidence 55%
22.00tok/s — on M5 Max via mlx BF16
our signed data: M5 Max
“DFlash support in oMLX 0.3.5 RC1 looks like it doubles (!!!) the speed of Qwen3.5 27B (BF16). Initial test. Generation T/S went from 9 to 22 T/S! Models used (HuggingFace) Main Model: Jackrong/MLX-Qwopus3.5-27B-v3-bf16 Draft Model: z-lab/Qwen3.5-27B-DFlash System: M5 Ma…”
- communityconfidence 55%
95.00tok/s — on M5 Max via llama.cpp Q4_0
our signed data: M5 Max
“benchmarks (7B Q4_0, tokens/sec):** - M3 Max (40-core): ~66 t/s - M4 Max (40-core): ~83 t/s - M5 Max: TBD (ships March 11), but expect ~90-95 t/s based on bandwidth scaling **Where the M5 really shines is prompt processing (time to first token).** The Neural Accelerators make t…”
Models measured on M5 Max
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