Builds / Apple M5 Ultra 64-core GPU (unified, Mac Studio)

Mac Studio M5 Ultra 96 GB (64-core GPU): gpt-oss 120B at 95 tok/s, 2,588 tok/s prefill at 15K (Ollama)

Last updated: 2 Nov 2026

Source: reddit.com ↗📏 How measured: reported by the original source; RigBase re-checked the numbers at that source

GPU(s): Apple M5 Ultra 64-core GPU (unified, Mac Studio)
VRAM / unified memory (GB): 96
CPU: Apple M5 Ultra (30-core CPU)
Motherboard: Mac Studio
Amazon · eBay (used) · Newegg
RAM: 96 GB unified memory
PSU: not stated
Other: macOS 27.2, headless; Ollama 0.40.13 (OLLAMA_CONTEXT_LENGTH=131072, FLASH_ATTENTION=1, KV_CACHE_TYPE=q8_0, NUM_PARALLEL=1); iogpu.wired_limit_mb=88064 (86 GB); every model 100% on GPU
Reported total cost: not stated
Power: not stated

Measured speed (generation tokens/sec)

Model	Quant	Engine	tok/s	Prompt processing (tok/s)	Details
gpt-oss 120B	MXFP4 (Ollama gpt-oss:120b, 65 GB)	Ollama 0.40.13 (llamacpp runner)	95	2,588 @ 15,000-token prompt est. TTFT @8k ≈ 3.2 s (calculated from measured prefill)	context: short prompts📏 Ollama API eval_count/eval_duration (load time excluded); single run, not an average; reported as 95 to 102, lower end listed. Decode after a ~60K prompt: 67 tok/s
Qwen3.8 27B	MXFP8 (Ollama qwen3.8:27b-mxfp8, 31 GB)	Ollama 0.40.13 (MLX runner)	47.2	1,376 @ 15,000-token prompt est. TTFT @8k ≈ 6.0 s (calculated from measured prefill)	context: short prompt📏 Ollama API eval_count/eval_duration (load time excluded); single run, not an average; 52.9 on a 6.6K prompt; 39 tok/s decode after a ~60K prompt
Qwen3.6 35B-A3B	Ollama qwen3.6:35b-a3b default tag (23 GB)	Ollama 0.40.13 (llamacpp runner)	246	not stated	context: short prompt📏 Ollama API eval_count/eval_duration (load time excluded); single run, not an average

Prompt processing = prefill speed (how fast the prompt is read) as reported by the source, with the prompt length it was measured at; blank = the source does not report it. est. TTFT @8k is calculated from the measured prefill speed (8,192 ÷ prefill tok/s), shown only when prefill was measured on a prompt of 512+ tokens. It is not a measurement and ignores load time and caching. measured TTFT is the source's own time-to-first-token. Why prefill matters for coding assistants.

r/LocalLLM post, 1 Nov 2026: the first published numbers we have for the 96 GB / 64-core M5 Ultra. Single runs. Same post: Qwen3.8 27B 8-bit in oMLX with an MTP head reached 41.4 tok/s cold and 79.1-89.5 warm (MTP, not comparable with plain decode); nemotron-3.5-super:120b-a12b-nvfp4 decoded at 110 and qwen3-coder-next:q4_K_M at 107 (models not in our database yet, so not listed as rows).

Source: https://www.reddit.com/r/LocalLLM/comments/1xlatgd/m5_ultra_96gb_64core_gpu_numbers_ollama_mlx/

Blank fields = not stated in the source. We never estimate build data; GPU street prices above are separately sourced and dated.

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Rent before you buy: try this model on a cloud GPU for a few dollars first: Runpod (referral link; sign up with Google to get the new-user bonus)

Quick answers: Can Apple M5 Ultra run Qwen3.6 35B-A3B? · Can Apple M5 Ultra run Qwen3.8 27B? · Can Apple M5 Ultra run gpt-oss 120B?