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Put two chips, runtimes, or runtime versions side by side. Everything else is held fixed or called out, so a ratio is worth exactly what the matched runs say.

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Runs compared: 175
Like-for-like pairs: 39
Chips available: 16

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Side A

Side B

Model

Runtime

Runtime version

Quantisation

Backend

Prefill size

Decode size

Cooldown

46 runs vs 129 runs · 39 like-for-like pairs

Held fixedPrefill: PP512Decode: TG128

These figures mix different models. Pick a model for a like-for-like comparison.
A side mixes quantisations. Pick a quantisation to compare the same weights.
The sides differ in cooldown, conditioning. Ratios may reflect benchmark protocol rather than the chip.

Chip comparison

Apple M4 Max vs Apple M5 Pro

Ratios read side A relative to side B. Geometric mean over 39 like-for-like pairs.

Decode: 1.49×+49.2%
Prefill: 0.61×-39.4%

Side A

Apple M4 Max

46 runs

2 contributors

Latest 20 Sep 2026

Decode · TG128: 136.4tok/s median
best 583.3 · worst 22.8
Prefill · PP512: 1,715tok/s median
best 9,998 · worst 221

Models: gemma-3-1b-it-Q4, gemma-3-1b-it-Q8, gemma-4-26B-A4B-it-Q4, gemma-4-26B-A4B-it-Q8, gemma-4-E2B-it-Q4, gemma-4-E2B-it-Q8, gemma-4-E4B-it-Q4, gemma-4-E4B-it-Q8, gpt-oss-20b-MXFP4, gpt-oss-20b-Q4, gpt-oss-20b-Q8, Llama-3.1-8B-Instruct-Q4, Llama-3.1-8B-Instruct-Q8, Llama-3.2-1B-Instruct-Q4, Llama-3.2-1B-Instruct-Q8, Llama-3.2-3B-Instruct-Q4, Llama-3.2-3B-Instruct-Q8, Mistral-7B-Instruct-v0.3-Q4, Mistral-7B-Instruct-v0.3-Q8, muse-glimmer-30B-kquant-17gb, muse-glimmer-30B-kquant-dynamic, NVIDIA-Nemotron-3-Nano-30B-A3B-Q4, Qwen3-0.6B-Q4, Qwen3-0.6B-Q8, Qwen3-1.7B-Q4, Qwen3-1.7B-Q8, Qwen3-30B-A3B-Instruct-2507-Q4, Qwen3-30B-A3B-Thinking-2507-Q4, Qwen3-4B-Instruct-2507-Q4, Qwen3-4B-Instruct-2507-Q8, Qwen3-4B-Q4, Qwen3-4B-Q8, Qwen3-4B-Thinking-2507-Q4, Qwen3-4B-Thinking-2507-Q8, Qwen3-8B-Q4, Qwen3-8B-Q8, Qwen3.5-2B-Base-Q4, Qwen3.5-2B-Base-Q8, Qwen3.5-2B-Q4, Qwen3.5-2B-Q8, Qwen3.5-35B-A3B-Q4, Qwen3.6-27B-Q4, Qwen3.6-35B-A3B-Q4, Qwen3.8-27B, Qwen3.8-27B-Q4, Qwen3.8-27B-Q4-mtp
Runtimes: BaseRTllama.cpp
Versions: BaseRT 0.2.4, BaseRT 0.2.6, llama.cpp b10809 (5266f24da)
Quantisations: mxfp4, passthrough_gguf, Q4, Q4_K_M, Q8
Backends: BLAS + Metal, Metal
Cooldown: off
Conditioning: runtime_native_warmup, warmup_only
Decode workload: TG128, TG128 @ 1 ctx
Harness schema: basert-benchmark-harness/1, computearena-measurements/1

Side B

Apple M5 Pro

129 runs

2 contributors

Latest 18 Sep 2026

Decode · TG128: 72.8tok/s median
best 2,068.0 · worst 10.6
Prefill · PP512: 1,852tok/s median
best 190,107 · worst 327

Models: gemma-3-1b-it, gemma-4-26B-A4B-it, gemma-4-E2B-it, gemma-4-E4B-it, Gemma-4-E4B-It, gpt-oss-20b, Gpt-Oss-20B, Llama 3.2 3B Instruct, Llama-3.1-8B-Instruct, Llama-3.2-1B-Instruct, Llama-3.2-3B-Instruct, Mistral-7B-Instruct-v0.3, Muse-Glimmer-30B, NVIDIA-Nemotron-3-Nano-30B-A3B, Qwen3-0.6B, Qwen3-1.7B, Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-4B, Qwen3-4B-Instruct-2507, Qwen3-4B-Thinking-2507, Qwen3-8B, Qwen3-8B-base-q2, Qwen3-8B-base-q3, Qwen3-8B-base-q5, Qwen3-8B-base-q6, Qwen3.5-2B, Qwen3.5-2B-Base, Qwen3.5-35B-A3B, Qwen3.6-27B, Qwen3.6-35B-A3B, Qwen3.8-27B, Tinystories Lay8 Hs512 Hd8 33M, tinystories-lay8-hs512-hd8-33M
Runtimes: BaseRTllama.cpp
Versions: BaseRT 0.2.4, BaseRT 0.2.5, llama.cpp b10809 (5266f24da), llama.cpp b9960 (a935fbffe)
Quantisations: BF16, F16, mxfp4, passthrough_gguf, Q2, Q2_K, Q3, Q3_K_M, Q4, Q4_0, Q4_1, Q4_K_M, Q5, Q5_K_M, Q6, Q6_K, Q8, Q8_0
Backends: BLAS + Metal, Metal
Cooldown: off, on
Conditioning: idle_reset_then_warm, runtime_native_warmup, warmup_only
Decode workload: TG128, TG128 @ 1 ctx
Harness schema: basert-benchmark-harness/1, computearena-measurements/1

What differs besides the chip

Facet	Apple M4 Max	Apple M5 Pro
Model	gemma-3-1b-it-Q4, gemma-3-1b-it-Q8, gemma-4-26B-A4B-it-Q4, gemma-4-26B-A4B-it-Q8, gemma-4-E2B-it-Q4, gemma-4-E2B-it-Q8, gemma-4-E4B-it-Q4, gemma-4-E4B-it-Q8, gpt-oss-20b-MXFP4, gpt-oss-20b-Q4, gpt-oss-20b-Q8, Llama-3.1-8B-Instruct-Q4, Llama-3.1-8B-Instruct-Q8, Llama-3.2-1B-Instruct-Q4, Llama-3.2-1B-Instruct-Q8, Llama-3.2-3B-Instruct-Q4, Llama-3.2-3B-Instruct-Q8, Mistral-7B-Instruct-v0.3-Q4, Mistral-7B-Instruct-v0.3-Q8, muse-glimmer-30B-kquant-17gb, muse-glimmer-30B-kquant-dynamic, NVIDIA-Nemotron-3-Nano-30B-A3B-Q4, Qwen3-0.6B-Q4, Qwen3-0.6B-Q8, Qwen3-1.7B-Q4, Qwen3-1.7B-Q8, Qwen3-30B-A3B-Instruct-2507-Q4, Qwen3-30B-A3B-Thinking-2507-Q4, Qwen3-4B-Instruct-2507-Q4, Qwen3-4B-Instruct-2507-Q8, Qwen3-4B-Q4, Qwen3-4B-Q8, Qwen3-4B-Thinking-2507-Q4, Qwen3-4B-Thinking-2507-Q8, Qwen3-8B-Q4, Qwen3-8B-Q8, Qwen3.5-2B-Base-Q4, Qwen3.5-2B-Base-Q8, Qwen3.5-2B-Q4, Qwen3.5-2B-Q8, Qwen3.5-35B-A3B-Q4, Qwen3.6-27B-Q4, Qwen3.6-35B-A3B-Q4, Qwen3.8-27B, Qwen3.8-27B-Q4, Qwen3.8-27B-Q4-mtp	gemma-3-1b-it, gemma-4-26B-A4B-it, gemma-4-E2B-it, gemma-4-E4B-it, Gemma-4-E4B-It, gpt-oss-20b, Gpt-Oss-20B, Llama 3.2 3B Instruct, Llama-3.1-8B-Instruct, Llama-3.2-1B-Instruct, Llama-3.2-3B-Instruct, Mistral-7B-Instruct-v0.3, Muse-Glimmer-30B, NVIDIA-Nemotron-3-Nano-30B-A3B, Qwen3-0.6B, Qwen3-1.7B, Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-4B, Qwen3-4B-Instruct-2507, Qwen3-4B-Thinking-2507, Qwen3-8B, Qwen3-8B-base-q2, Qwen3-8B-base-q3, Qwen3-8B-base-q5, Qwen3-8B-base-q6, Qwen3.5-2B, Qwen3.5-2B-Base, Qwen3.5-35B-A3B, Qwen3.6-27B, Qwen3.6-35B-A3B, Qwen3.8-27B, Tinystories Lay8 Hs512 Hd8 33M, tinystories-lay8-hs512-hd8-33M
Runtime version	BaseRT 0.2.4, BaseRT 0.2.6, llama.cpp b10809 (5266f24da)	BaseRT 0.2.4, BaseRT 0.2.5, llama.cpp b10809 (5266f24da), llama.cpp b9960 (a935fbffe)
Quantisation	mxfp4, passthrough_gguf, Q4, Q4_K_M, Q8	BF16, F16, mxfp4, passthrough_gguf, Q2, Q2_K, Q3, Q3_K_M, Q4, Q4_0, Q4_1, Q4_K_M, Q5, Q5_K_M, Q6, Q6_K, Q8, Q8_0
Cooldown	off	off, on
Conditioning	runtime_native_warmup, warmup_only	idle_reset_then_warm, runtime_native_warmup, warmup_only

Like-for-like pairs

Each row is a configuration present on both sides. Values are per-cell medians; ratios read Apple M4 Max relative to Apple M5 Pro.

Configuration	Decode A	Decode B	Ratio	Prefill A	Prefill B	Ratio	Runs A / B
Qwen3-8B BaseRTQ4	99.2	59.7	1.66×	943	1,761	0.54×	1 / 7
Llama-3.2-3B-Instruct BaseRTQ4	155.9	94.6	1.65×	2,210	4,297	0.51×	2 / 5
Llama-3.1-8B-Instruct BaseRTQ4	83.2	48.8	1.70×	946	1,784	0.53×	2 / 4
Llama-3.2-1B-Instruct BaseRTQ4	346.1	206.3	1.68×	6,011	11,673	0.51×	2 / 3
Mistral-7B-Instruct-v0.3 BaseRTQ4	87.2	51.7	1.69×	946	1,789	0.53×	2 / 2
Muse-Glimmer-30B BaseRTpassthrough_gguf	25.0	15.7	1.60×	260	480	0.54×	2 / 2
Qwen3-0.6B BaseRTQ4	583.3	512.0	1.14×	9,998	20,603	0.49×	1 / 3
Qwen3-0.6B BaseRTQ8	455.8	349.8	1.30×	9,749	20,366	0.48×	1 / 3
gemma-3-1b-it BaseRTQ4	361.2	281.7	1.28×	7,448	14,392	0.52×	1 / 2
gemma-4-E4B-it BaseRTQ4	109.3	82.3	1.33×	2,552	4,646	0.55×	1 / 2
gemma-4-E4B-it BaseRTQ8	77.4	51.2	1.51×	2,481	4,476	0.55×	1 / 2
gpt-oss-20b BaseRTQ4	160.5	110.5	1.45×	1,709	1,156	1.48×	1 / 2
Qwen3-1.7B BaseRTQ4	318.4	222.9	1.43×	4,146	7,654	0.54×	1 / 2
Qwen3-1.7B BaseRTQ8	224.1	132.4	1.69×	4,025	7,655	0.53×	1 / 2
Qwen3-30B-A3B-Instruct-2507 BaseRTQ4	139.8	98.1	1.43×	1,789	3,493	0.51×	1 / 2
Qwen3-4B BaseRTQ4	157.5	105.6	1.49×	1,725	3,378	0.51×	1 / 2
Qwen3-4B-Instruct-2507 BaseRTQ4	121.9	89.9	1.36×	1,711	3,345	0.51×	1 / 2
Qwen3-4B-Instruct-2507 BaseRTQ8	104.3	63.5	1.64×	1,723	3,402	0.51×	1 / 2
Qwen3.8-27B BaseRTQ4	31.6	13.5	2.34×	222	341	0.65×	1 / 2
Qwen3-8B BaseRTQ8	59.6	34.9	1.71×	942	1,746	0.54×	1 / 2
gemma-3-1b-it BaseRTQ8	281.0	210.3	1.34×	7,527	15,061	0.50×	1 / 1
gemma-4-26B-A4B-it BaseRTQ4	81.5	51.7	1.58×	1,664	2,047	0.81×	1 / 1
gemma-4-26B-A4B-it BaseRTQ8	73.8	51.4	1.43×	1,679	3,253	0.52×	1 / 1
gemma-4-E2B-it BaseRTQ4	175.3	144.6	1.21×	7,670	13,240	0.58×	1 / 1
gemma-4-E2B-it BaseRTQ8	132.2	96.0	1.38×	7,448	12,646	0.59×	1 / 1
gpt-oss-20b BaseRTQ8	146.9	100.6	1.46×	1,700	1,065	1.60×	1 / 1
gpt-oss-20b BaseRTmxfp4	146.7	103.6	1.42×	1,715	1,013	1.69×	1 / 1
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 BaseRTQ4	169.1	100.1	1.69×	1,632	2,499	0.65×	1 / 1
Qwen3-30B-A3B-Thinking-2507 BaseRTQ4	140.1	104.4	1.34×	1,797	3,694	0.49×	1 / 1
Qwen3-4B BaseRTQ8	105.4	64.7	1.63×	1,712	3,412	0.50×	1 / 1
Qwen3-4B-Thinking-2507 BaseRTQ4	122.7	91.6	1.34×	1,721	3,363	0.51×	1 / 1
Qwen3-4B-Thinking-2507 BaseRTQ8	104.3	64.7	1.61×	1,714	3,414	0.50×	1 / 1
Qwen3.5-2B BaseRTQ4	301.7	217.7	1.39×	1,718	2,641	0.65×	1 / 1
Qwen3.5-2B BaseRTQ8	205.4	130.7	1.57×	1,721	2,638	0.65×	1 / 1
Qwen3.5-2B-Base BaseRTQ4	295.1	218.2	1.35×	1,713	2,645	0.65×	1 / 1
Qwen3.5-2B-Base BaseRTQ8	208.0	131.4	1.58×	1,727	2,642	0.65×	1 / 1
Qwen3.5-35B-A3B BaseRTQ4	140.1	113.4	1.24×	895	1,304	0.69×	1 / 1
Qwen3.6-27B BaseRTQ4	30.3	16.1	1.88×	221	364	0.61×	1 / 1
Qwen3.6-35B-A3B BaseRTQ4	139.7	113.2	1.23×	886	1,275	0.69×	1 / 1

Side A · Apple M4 Max

Top 25 of 46 runs by decode throughput.

Model / format	Chip / backend	Decode	Prefill	Contributor	Date	Report
Qwen3-0.6B-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	583.3 TG128 @ 1 ctx	9,998 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3-0.6B-Q8 BaseRT 0.2.6Q8	Apple M4 Max Metal	455.8 TG128 @ 1 ctx	9,749 PP512	basecompute	19 Sep 2026	View Benchmark
Llama-3.2-1B-Instruct-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	371.7 TG128 @ 1 ctx	5,963 PP512	basecompute	19 Sep 2026	View Benchmark
gemma-3-1b-it-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	361.2 TG128 @ 1 ctx	7,448 PP512	basecompute	19 Sep 2026	View Benchmark
Llama-3.2-1B-Instruct-Q8 BaseRT 0.2.6Q4	Apple M4 Max Metal	320.5 TG128 @ 1 ctx	6,058 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3-1.7B-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	318.4 TG128 @ 1 ctx	4,146 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-2B-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	301.7 TG128 @ 1 ctx	1,718 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-2B-Base-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	295.1 TG128 @ 1 ctx	1,713 PP512	basecompute	19 Sep 2026	View Benchmark
gemma-3-1b-it-Q8 BaseRT 0.2.6Q8	Apple M4 Max Metal	281.0 TG128 @ 1 ctx	7,527 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3-1.7B-Q8 BaseRT 0.2.6Q8	Apple M4 Max Metal	224.1 TG128 @ 1 ctx	4,025 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-2B-Base-Q8 BaseRT 0.2.6Q8	Apple M4 Max Metal	208.0 TG128 @ 1 ctx	1,727 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-2B-Q8 BaseRT 0.2.6Q8	Apple M4 Max Metal	205.4 TG128 @ 1 ctx	1,721 PP512	basecompute	19 Sep 2026	View Benchmark
Llama-3.2-3B-Instruct-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	178.8 TG128 @ 1 ctx	2,202 PP512	basecompute	19 Sep 2026	View Benchmark
gemma-4-E2B-it-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	175.3 TG128 @ 1 ctx	7,670 PP512	basecompute	19 Sep 2026	View Benchmark
NVIDIA-Nemotron-3-Nano-30B-A3B-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	169.1 TG128 @ 1 ctx	1,632 PP512	basecompute	20 Sep 2026	View Benchmark
gpt-oss-20b-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	160.5 TG128 @ 1 ctx	1,709 PP512	basecompute	20 Sep 2026	View Benchmark
Qwen3-4B-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	157.5 TG128 @ 1 ctx	1,725 PP512	basecompute	19 Sep 2026	View Benchmark
gpt-oss-20b-Q8 BaseRT 0.2.6Q8	Apple M4 Max Metal	146.9 TG128 @ 1 ctx	1,700 PP512	basecompute	20 Sep 2026	View Benchmark
gpt-oss-20b-MXFP4 BaseRT 0.2.6mxfp4	Apple M4 Max Metal	146.7 TG128 @ 1 ctx	1,715 PP512	basecompute	20 Sep 2026	View Benchmark
Qwen3-30B-A3B-Thinking-2507-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	140.1 TG128 @ 1 ctx	1,797 PP512	basecompute	20 Sep 2026	View Benchmark
Qwen3.5-35B-A3B-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	140.1 TG128 @ 1 ctx	895 PP512	basecompute	20 Sep 2026	View Benchmark
Qwen3-30B-A3B-Instruct-2507-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	139.8 TG128 @ 1 ctx	1,789 PP512	basecompute	20 Sep 2026	View Benchmark
Qwen3.6-35B-A3B-Q4 BaseRT 0.2.6Q4	Apple M4 Max Metal	139.7 TG128 @ 1 ctx	886 PP512	basecompute	20 Sep 2026	View Benchmark
Llama-3.2-3B-Instruct-Q8 BaseRT 0.2.6Q4	Apple M4 Max Metal	133.1 TG128 @ 1 ctx	2,218 PP512	basecompute	19 Sep 2026	View Benchmark
gemma-4-E2B-it-Q8 BaseRT 0.2.6Q8	Apple M4 Max Metal	132.2 TG128 @ 1 ctx	7,448 PP512	basecompute	19 Sep 2026	View Benchmark

Side B · Apple M5 Pro

Top 25 of 129 runs by decode throughput.

Model / format	Chip / backend	Decode	Prefill	Contributor	Date	Report
ivnle/tinystories-lay8-hs512-hd8-33M BaseRT 0.2.4Q4	Apple M5 Pro Metal	2,068.0 TG128	190,107 PP512	arki05	17 Sep 2026	View Benchmark
Tinystories Lay8 Hs512 Hd8 33M llama.cpp b10809 (5266f24da)Q4_0	Apple M5 Pro BLAS + Metal	1,443.3 TG128	124,181 PP512	arki05	17 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q4	Apple M5 Pro Metal	516.8 TG128	20,778 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q4	Apple M5 Pro Metal	512.0 TG128	20,603 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.5Q4	Apple M5 Pro Metal	481.5 TG128 @ 1 ctx	19,595 PP512	isu	18 Sep 2026	View Benchmark
Qwen3-0.6B llama.cpp b10809 (5266f24da)Q4_K_M	Apple M5 Pro BLAS + Metal	358.3 TG128	14,509 PP512	arki05	11 Sep 2026	View Benchmark
Qwen3-0.6B llama.cpp b10809 (5266f24da)Q4_K_M	Apple M5 Pro BLAS + Metal	358.1 TG128	14,344 PP512	arki05	11 Sep 2026	View Benchmark
Qwen3-0.6B llama.cpp b10809 (5266f24da)Q4_K_M	Apple M5 Pro BLAS + Metal	356.2 TG128	14,552 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q8	Apple M5 Pro Metal	350.2 TG128	20,537 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q8	Apple M5 Pro Metal	349.8 TG128	20,366 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q8	Apple M5 Pro Metal	311.0 TG128	18,474 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/gemma-3-1b-it BaseRT 0.2.4Q4	Apple M5 Pro Metal	293.1 TG128	15,066 PP512	arki05	11 Sep 2026	View Benchmark
Qwen3-0.6B llama.cpp b10809 (5266f24da)Q8_0	Apple M5 Pro BLAS + Metal	283.0 TG128	14,942 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/gemma-3-1b-it BaseRT 0.2.4Q4	Apple M5 Pro Metal	270.4 TG128	13,717 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-1.7B BaseRT 0.2.4Q4	Apple M5 Pro Metal	235.4 TG128	8,040 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Llama-3.2-1B-Instruct BaseRT 0.2.4Q4	Apple M5 Pro Metal	229.1 TG128	11,673 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3.5-2B-Base BaseRT 0.2.4Q4	Apple M5 Pro Metal	218.2 TG128	2,645 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3.5-2B BaseRT 0.2.4Q4	Apple M5 Pro Metal	217.7 TG128	2,641 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-1.7B BaseRT 0.2.4Q4	Apple M5 Pro Metal	210.3 TG128	7,268 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/gemma-3-1b-it BaseRT 0.2.4Q8	Apple M5 Pro Metal	210.3 TG128	15,061 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Llama-3.2-1B-Instruct BaseRT 0.2.4Q4	Apple M5 Pro Metal	206.3 TG128	10,528 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Llama-3.2-1B-Instruct BaseRT 0.2.4Q4	Apple M5 Pro Metal	205.3 TG128	12,067 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/gemma-4-E2B-it BaseRT 0.2.4Q4	Apple M5 Pro Metal	144.6 TG128	13,240 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-1.7B BaseRT 0.2.4Q8	Apple M5 Pro Metal	139.7 TG128	8,043 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3.5-2B-Base BaseRT 0.2.4Q8	Apple M5 Pro Metal	131.4 TG128	2,642 PP512	arki05	11 Sep 2026	View Benchmark