llamaperf

M4 Pro 64GB

APPLE · 64GB unified memory · 33 reports

On the M4 Pro 64GB, Qwen3.8 27B at 4-bit, the model most run on it, generates a median of 12.9 tokens per second from 4 plain runs (community reports, as of 30 Oct 2026).

As of 30 Oct 2026, the models most run on the M4 Pro 64GB, with generation and prompt-processing medians from plain runs (one device, one request, no speculative decoding):

Engines people use on it: llama.cpp 14 (reports) · MLX 1 (reports)

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Reported performance on the M4 Pro 64GB

These reports mix GPU counts and memory setups, so a model listed here may have needed extra cards or CPU offloading. The calculator estimates which models fit in 64 GB of unified memory.

Showing 20 of 33 reports. See all of them →

gpt-oss 21B (3.6B active)

M4 Pro 64GB · llama.cpp b6280 / 0fd90db / Metal

Apple hardware
generation:
63.3 tokens/s
prompt processing (prefill):
700.8 tokens/s
quant:
MXFP4 (GGUF)
flash attention:
on
mtp (multi-token prediction):
off

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports gpt-oss 20B MXFP4 at 63.3 t/s generation and 700.8 t/s prompt processing on a Mac mini M4 Pro with 64 GB unified memory. Setup is llama.cpp build 6280 with MXFP4 GGUF, F16 K/V cache, flash attention, one Metal device, and -ngl 99. The 63.3 t/s is a separate TG128 depth-zero test, not generation after the four large prompts; prompt rates fall from 700.78 t/s at 2048 tokens to 419.74 t/s at 32768.

Oct 14, 2026
Apple hardware
generation:
70.9 tokens/s
prompt processing (prefill):
1245.6 tokens/s
quant:
Q8_0 (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen2.5-Omni-3B at 70.9 t/s generation and 1245.6 t/s prompt processing on an Apple M4 Pro with 64 GB unified memory. Setup is dasLLAMA version 16 with Q8_0 weights, f16 K/V cache, and one Metal GPU; the run used 512 synthetic prompt positions and 128 serial decode positions. The figures are engine-side loop wall time, not a 512-input/128-output chat request, and no speculative decoding was used.

Oct 13, 2026
Apple hardware
generation:
54.2 tokens/s
prompt processing (prefill):
957.9 tokens/s
quant:
Q8_0 (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen3-VL-4B-Instruct at 54.2 t/s generation and 957.9 t/s prompt processing on an M4 Pro with 64 GB unified memory. Setup is dasLLAMA with Q8_0 weights, q8 activations, f16 scales and f16 KV cache, one GPU, 640 positions allocated. The figures are native synthetic counts: 512 prompt positions and 128 serial decode positions, not a single chat request.

Oct 13, 2026
Apple hardware
generation:
13.0 tokens/s
prompt processing (prefill):
125.6 tokens/s
quant:
UD-IQ4_XS (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen3.8-27B at 13 t/s generation and 125.6 t/s prompt processing on an M4 Pro with 64 GB unified memory. Setup is dasLLAMA version 16 with UD-IQ4_XS weights and f16 KV cache, one GPU, 640 positions allocated. The figures are native PP512 and TG128 means over five repetitions, not one 512-input/128-output request.

Oct 13, 2026
Apple hardware
generation:
13.1 tokens/s
prompt processing (prefill):
126.2 tokens/s
quant:
Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen3.8-27B at 13.1 t/s generation and 126.2 t/s prompt processing on an M4 Pro with 64 GB unified memory. Setup is dasLLAMA version 16 with Q4_K_M weights, f16 KV cache, and one Metal GPU. The figures are native PP512 and TG128 means over five repetitions, not a single chat request; the session allocates 640 positions.

Oct 13, 2026
Apple hardware
generation:
86.6 tokens/s
prompt processing (prefill):
1545.7 tokens/s
quant:
Q8_0 (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Gemma 4 E2B at 86.6 t/s generation and 1545.7 t/s prompt processing on an M4 Pro with 64GB unified memory. Setup is dasLLAMA with Q8_0 weights, f16 KV cache, and one Metal GPU; the session requested 640 positions and no speculative decoding. The figures are native PP512 and TG128 counts over five repetitions, not one 512-in/128-out request.

Oct 13, 2026
Apple hardware
generation:
17.6 tokens/s
prompt processing (prefill):
143.2 tokens/s
quant:
Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Mistral Small 3.1 24B at 17.6 t/s generation and 143.2 t/s prompt processing on an Apple M4 Pro with 64 GB unified memory. Setup is dasLLAMA version 16 with Q4_K_M weights, f16 KV cache, and one Metal GPU at 99 layers offloaded. The figures are native PP512 and TG128 counts, not a single 512-input/128-output request; the session requests 640 positions of context.

Oct 13, 2026
Apple hardware
generation:
94.5 tokens/s
prompt processing (prefill):
806.0 tokens/s
quant:
Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen3-30B-A3B-Instruct-2507 at 94.5 t/s generation and 806 t/s prompt processing on an M4 Pro with 64 GB unified memory. Setup is dasLLAMA version 16 with Q4_K_M weights, f16 KV cache, and one Metal GPU, with 640 positions allocated. The figures are native PP512 and TG128 means over five repetitions, not a single 512-input/128-output request.

Oct 13, 2026
Apple hardware
generation:
83.2 tokens/s
prompt processing (prefill):
862.6 tokens/s
quant:
MXFP4 (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports gpt-oss-20b at 83.2 t/s generation and 862.6 t/s prompt processing on an Apple M4 Pro with 64 GB unified memory. Setup is dasLLAMA version 16 with MXFP4 weights, q8 activations, f16 scales and f16 KV cache, one Metal GPU with 99 layers offloaded. Figures are native PP512 and TG128 counts over five repetitions, not a single 512-input/128-output request; the session requests 640 positions of context.

Oct 13, 2026
Apple hardware
generation:
46.2 tokens/s
prompt processing (prefill):
770.2 tokens/s
quant:
Q8_0 (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Gemma 4 E4B at 46.2 t/s generation and 770.2 t/s prompt processing on an M4 Pro 64GB. Setup is dasLLAMA with Q8_0 weights, f16 KV cache, and one Metal GPU, with 640 positions allocated. The figures are native PP512 and TG128 counts, not one 512-input/128-output request, and no longer-context performance is inferred.

Oct 13, 2026
Apple hardware
generation:
30.9 tokens/s
prompt processing (prefill):
298.5 tokens/s
quant:
Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Gemma 4 12B at 30.9 t/s generation and 298.5 t/s prompt processing on an M4 Pro with 64 GB unified memory. Setup is dasLLAMA version 16 with Q4_K_M weights and f16 KV cache, one GPU, 640 positions allocated. The figures are native PP512 and TG128 means over five repetitions, not a single 512-input/128-output request; a separate four-stream run reached 34.18746480210349 t/s summed scheduler throughput.

Oct 13, 2026
Apple hardware
generation:
60.4 tokens/s
prompt processing (prefill):
818.7 tokens/s
quant:
UD-Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Gemma 4 26B-A4B-it at 60.4 t/s generation and 818.7 t/s prompt processing on an M4 Pro with 64 GB unified memory. Setup is dasLLAMA 0.6.4 with UD-Q4_K_M weights, f16 KV cache, and one Metal GPU, with 640 positions allocated. The figures are native PP512 and TG128 counts over five repetitions, not a single 512-input/128-output request.

Oct 13, 2026
Apple hardware
generation:
12.9 tokens/s
prompt processing (prefill):
126.4 tokens/s
quant:
Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen3.6 27B at 12.9 t/s generation and 126.4 t/s prompt processing on an M4 Pro with 64 GB unified memory. Setup is dasLLAMA version 16 with Q4_K_M weights, f16 K/V cache, and one Metal GPU; the run used 512 synthetic prompt positions and 128 serial decode positions. The user notes these are native processing counts, not one 512-input/128-output chat request, and that no speculative decoding was requested.

Oct 13, 2026
Apple hardware
generation:
66.4 tokens/s
prompt processing (prefill):
787.1 tokens/s
quant:
UD-Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen3.6-35B-A3B at 66.4 t/s generation and 787.1 t/s prompt processing on an Apple M4 Pro with 64 GB unified memory. Setup is dasLLAMA version 16 with UD-Q4_K_M weights, f16 K/V cache, and one Metal GPU at 640 allocated positions. The figures are native PP512 and TG128 counts, not a single 512-input/128-output request.

Oct 13, 2026
Apple hardware
generation:
67.5 tokens/s
prompt processing (prefill):
1158.7 tokens/s
quant:
Q8_0 (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen2.5-Omni-3B at 67.5 t/s generation and 1158.7 t/s prompt processing on an M4 Pro with 64 GB unified memory. Setup is llama.cpp commit 6fdd0ac89 with Q8_0 weights, Metal backend, -ngl 99, 10 CPU threads, and f16 K/V cache. The figures are separate native tests from cleared state: PP512 and TG128, each at zero pre-existing depth, not one 512-input/128-output request.

Oct 13, 2026
Apple hardware
generation:
53.6 tokens/s
prompt processing (prefill):
878.7 tokens/s
quant:
Q8_0 (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen3-VL-4B-Instruct at 53.6 t/s generation and 878.7 t/s prompt processing on an M4 Pro 64GB. Setup is llama.cpp commit 6fdd0ac89 with Q8_0 weights, Metal backend, -ngl 99, and 10 CPU threads on one physical GPU. The figures are native llama-bench PP512 and TG128 means over five repetitions at depth 0, not a single 512-input/128-output request.

Oct 13, 2026
Apple hardware
generation:
12.8 tokens/s
prompt processing (prefill):
126.1 tokens/s
quant:
UD-IQ4_XS (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen3.8-27B at 12.8 t/s generation and 126.1 t/s prompt processing on an Apple M4 Pro with 64 GB unified memory. Setup is llama.cpp commit 6fdd0ac89 with the UD-IQ4_XS GGUF, Metal backend, -ngl 99, 10 CPU threads, and K/V cache in f16. The figures are native llama-bench means over five repetitions: PP512 at 126.094155 t/s and TG128 at 12.841348 t/s, each starting from cleared state at depth 0, with model load and warmup excluded.

Oct 13, 2026
Apple hardware
generation:
11.5 tokens/s
prompt processing (prefill):
126.3 tokens/s
quant:
Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Qwen3.8-27B at 11.5 t/s generation and 126.3 t/s prompt processing on an M4 Pro 64GB. Setup is llama.cpp commit 6fdd0ac89 with Q4_K_M, Metal backend, -ngl 99, 10 CPU threads, and K/V f16. The native figures are PP512 and TG128 from cleared state at depth 0, not a single 512-input/128-output request.

Oct 13, 2026
Apple hardware
generation:
78.2 tokens/s
prompt processing (prefill):
1558.9 tokens/s
quant:
Q8_0 (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Gemma 4 E2B at 78.2 t/s generation and 1558.9 t/s prompt processing on an M4 Pro 64GB. Setup is llama.cpp with Q8_0 weights, Metal backend, -ngl 99, and 10 CPU threads on one physical GPU. The figures are native llama-bench PP512 and TG128 means over five repetitions at depth 0, not a single 512-input/128-output request.

Oct 13, 2026
Apple hardware
generation:
17.4 tokens/s
prompt processing (prefill):
146.8 tokens/s
quant:
Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports Mistral Small 3.1 24B at 17.4 t/s generation and 146.8 t/s prompt processing on an M4 Pro with 64 GB unified memory. Setup is llama.cpp commit 6fdd0ac89 with Q4_K_M weights, Metal backend, -ngl 99, 10 CPU threads, and f16 K/V cache. The figures are native llama-bench PP512 and TG128 means over five repetitions at depth 0, not a single chat request.

Oct 13, 2026

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Reported configurations

Individual observations from this page, not expected speeds or a ranking. Select a model to open its report, with the source and offloading conditions. Reported context may be a limit rather than actual input length.

Model / hardwareQuant / engineReported contextGeneration
gpt-oss 21B (3.6B active)
M4 Pro 64GB
MXFP4
llama.cpp b6280 / 0fd90db / Metal
Not reported63.3 tokens/s
Qwen2.5-Omni 3B
M4 Pro 64GB
Q8_0
dasLLAMA
Not reported70.9 tokens/s
Qwen3-VL 4B
M4 Pro 64GB
Q8_0
dasLLAMA
Not reported54.2 tokens/s
Qwen3.8 27B
M4 Pro 64GB
UD-IQ4_XS
dasLLAMA
Not reported13.0 tokens/s
Qwen3.8 27B
M4 Pro 64GB
Q4_K_M
dasLLAMA
Not reported13.1 tokens/s
Gemma 4 5.1B E2B
M4 Pro 64GB
Q8_0
dasLLAMA
Not reported86.6 tokens/s

Frequently asked

How fast is the M4 Pro 64GB for local LLMs?

On the M4 Pro 64GB, Qwen3.8 27B at 4-bit, the model most run on it, generates a median of 12.9 tokens per second from 4 plain runs (community reports, as of 30 Oct 2026). Also reported: gpt-oss 21B · 3.6B active at 4-bit (73.6 t/s, 3 plain runs); Gemma 4 12B at 4-bit (30.0 t/s, 2 plain runs).

What is the biggest model reported running on the M4 Pro 64GB?

The largest model with a plain generation run on the M4 Pro 64GB is Qwen3.6 35B · 3B active at 4-bit (57.5 t/s, 2 plain runs), as of 30 Oct 2026. Plain means one device, one request, no speculative decoding, so a run split across several cards does not count.