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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: 200
Like-for-like pairs: 28
Chips available: 16

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Side A

Side B

Model

Runtime

Runtime version

Quantisation

Backend

Prefill size

Decode size

Cooldown

71 runs vs 129 runs · 28 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, harness schema. Ratios may reflect benchmark protocol rather than the chip.

Chip comparison

NVIDIA GB10 vs Apple M5 Pro

Ratios read side A relative to side B. Geometric mean over 28 like-for-like pairs.

Decode: 0.76×-24.4%
Prefill: 2.28×+128%

Side A

NVIDIA GB10

71 runs

1 contributor

Latest 19 Sep 2026

Decode · TG128: 61.2tok/s median
best 458.8 · worst 7.5
Prefill · PP512: 4,738tok/s median
best 52,750 · worst 344

Models: gemma-3-1b-it-cuda-q4mix, gemma-3-1b-it-cuda-q8, gemma-3-1b-it-Q4, gemma-4-26B-A4B-it-cuda-q4mix, gemma-4-26B-A4B-it-cuda-q8, gemma-4-26B-A4B-it-Q4, gemma-4-E2B-it-cuda-q4mix, gemma-4-E2B-it-cuda-q8, gemma-4-E2B-it-Q4, gemma-4-E4B-it-Q4, gpt-oss-120b, gpt-oss-120b-MXFP4, gpt-oss-120b-Q4, gpt-oss-120b-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-cuda-q4mix, Llama-3.2-1B-Instruct-cuda-q8, Llama-3.2-1B-Instruct-Q4, Llama-3.2-1B-Instruct-Q8, Llama-3.2-3B-Instruct-cuda-q4mix, Llama-3.2-3B-Instruct-cuda-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, NVIDIA-Nemotron-3-Nano-30B-A3B-Q4, Qwen3-0.6B-cuda-q4, Qwen3-0.6B-cuda-q4mix, Qwen3-0.6B-cuda-q8, Qwen3-0.6B-Q4, Qwen3-1.7B-Q4, Qwen3-30B-A3B-Instruct-2507-cuda-q4mix, Qwen3-30B-A3B-Instruct-2507-cuda-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-Thinking-2507-Q4, Qwen3-4B-Thinking-2507-Q8, Qwen3-8B-Q4, Qwen3.5-122B-A10B-Q4, Qwen3.5-2B-Base-Q4, Qwen3.5-2B-cuda-q4mix, Qwen3.5-2B-cuda-q8, Qwen3.5-2B-Q4, Qwen3.5-35B-A3B-cuda-q4mix, Qwen3.5-35B-A3B-cuda-q8, Qwen3.5-35B-A3B-Q4, Qwen3.6-27B, Qwen3.6-27B-cuda-q4mix, Qwen3.6-27B-cuda-q8, Qwen3.6-27B-Q4, Qwen3.6-35B-A3B-cuda-q4mix, Qwen3.6-35B-A3B-cuda-q8, Qwen3.6-35B-A3B-Q4, Qwen3.8-27B-Q4, Qwen3.8-27B-Q4-mtp, Qwen3.8-27B-Q8
Runtimes: BaseRT
Versions: BaseRT 0.2.4, BaseRT 0.2.6
Quantisations: mxfp4, Q4, Q8
Backends: CUDA
Cooldown: off
Conditioning: warmup_only
Decode workload: TG128, TG128 @ 1 ctx
Harness schema: basert-benchmark-harness/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	NVIDIA GB10	Apple M5 Pro
Model	gemma-3-1b-it-cuda-q4mix, gemma-3-1b-it-cuda-q8, gemma-3-1b-it-Q4, gemma-4-26B-A4B-it-cuda-q4mix, gemma-4-26B-A4B-it-cuda-q8, gemma-4-26B-A4B-it-Q4, gemma-4-E2B-it-cuda-q4mix, gemma-4-E2B-it-cuda-q8, gemma-4-E2B-it-Q4, gemma-4-E4B-it-Q4, gpt-oss-120b, gpt-oss-120b-MXFP4, gpt-oss-120b-Q4, gpt-oss-120b-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-cuda-q4mix, Llama-3.2-1B-Instruct-cuda-q8, Llama-3.2-1B-Instruct-Q4, Llama-3.2-1B-Instruct-Q8, Llama-3.2-3B-Instruct-cuda-q4mix, Llama-3.2-3B-Instruct-cuda-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, NVIDIA-Nemotron-3-Nano-30B-A3B-Q4, Qwen3-0.6B-cuda-q4, Qwen3-0.6B-cuda-q4mix, Qwen3-0.6B-cuda-q8, Qwen3-0.6B-Q4, Qwen3-1.7B-Q4, Qwen3-30B-A3B-Instruct-2507-cuda-q4mix, Qwen3-30B-A3B-Instruct-2507-cuda-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-Thinking-2507-Q4, Qwen3-4B-Thinking-2507-Q8, Qwen3-8B-Q4, Qwen3.5-122B-A10B-Q4, Qwen3.5-2B-Base-Q4, Qwen3.5-2B-cuda-q4mix, Qwen3.5-2B-cuda-q8, Qwen3.5-2B-Q4, Qwen3.5-35B-A3B-cuda-q4mix, Qwen3.5-35B-A3B-cuda-q8, Qwen3.5-35B-A3B-Q4, Qwen3.6-27B, Qwen3.6-27B-cuda-q4mix, Qwen3.6-27B-cuda-q8, Qwen3.6-27B-Q4, Qwen3.6-35B-A3B-cuda-q4mix, Qwen3.6-35B-A3B-cuda-q8, Qwen3.6-35B-A3B-Q4, Qwen3.8-27B-Q4, Qwen3.8-27B-Q4-mtp, Qwen3.8-27B-Q8	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	BaseRT	BaseRT, llama.cpp
Runtime version	BaseRT 0.2.4, BaseRT 0.2.6	BaseRT 0.2.4, BaseRT 0.2.5, llama.cpp b10809 (5266f24da), llama.cpp b9960 (a935fbffe)
Quantisation	mxfp4, Q4, 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
Backend	CUDA	BLAS + Metal, Metal
Cooldown	off	off, on
Conditioning	warmup_only	idle_reset_then_warm, runtime_native_warmup, warmup_only
Harness schema	basert-benchmark-harness/1	basert-benchmark-harness/1, computearena-measurements/1

Like-for-like pairs

Each row is a configuration present on both sides. Values are per-cell medians; ratios read NVIDIA GB10 relative to Apple M5 Pro.

Configuration	Decode A	Decode B	Ratio	Prefill A	Prefill B	Ratio	Runs A / B
Llama-3.2-3B-Instruct-cuda-q8 BaseRTQ4	79.6	94.6	0.84×	14,761	4,297	3.44×	4 / 5
Qwen3-8B BaseRTQ4	50.3	59.7	0.84×	1,452	1,761	0.82×	1 / 7
Llama-3.2-1B-Instruct-cuda-q8 BaseRTQ4	184.5	206.3	0.89×	35,119	11,673	3.01×	4 / 3
Qwen3-0.6B BaseRTQ4	434.6	512.0	0.85×	50,619	20,603	2.46×	4 / 3
Llama-3.1-8B-Instruct BaseRTQ4	39.3	48.8	0.80×	7,433	1,784	4.17×	2 / 4
Qwen3-30B-A3B-Instruct-2507-cuda-q8 BaseRTQ4	70.9	98.1	0.72×	7,284	3,493	2.09×	4 / 2
Qwen3.6-27B-cuda-q8 BaseRTQ4	12.3	16.1	0.76×	1,140	364	3.13×	5 / 1
gemma-3-1b-it-cuda-q8 BaseRTQ4	268.0	281.7	0.95×	33,641	14,392	2.34×	3 / 2
Qwen3.5-35B-A3B-cuda-q8 BaseRTQ4	69.5	113.4	0.61×	2,437	1,304	1.87×	4 / 1
Qwen3.6-35B-A3B-cuda-q8 BaseRTQ4	64.1	113.2	0.57×	2,185	1,275	1.71×	4 / 1
Qwen3.8-27B BaseRTQ4	7.6	13.5	0.56×	1,131	341	3.32×	3 / 2
gemma-4-26B-A4B-it-cuda-q8 BaseRTQ4	44.0	51.7	0.85×	6,179	2,047	3.02×	3 / 1
gemma-4-E2B-it-cuda-q8 BaseRTQ4	87.7	144.6	0.61×	18,372	13,240	1.39×	3 / 1
Mistral-7B-Instruct-v0.3 BaseRTQ4	40.2	51.7	0.78×	7,532	1,789	4.21×	2 / 2
Qwen3.5-2B-cuda-q8 BaseRTQ4	151.5	217.7	0.70×	6,754	2,641	2.56×	3 / 1
gemma-4-E4B-it BaseRTQ4	71.3	82.3	0.87×	3,674	4,646	0.79×	1 / 2
gpt-oss-20b BaseRTQ4	57.3	110.5	0.52×	4,292	1,156	3.71×	1 / 2
Qwen3-1.7B BaseRTQ4	202.5	222.9	0.91×	7,394	7,654	0.97×	1 / 2
Qwen3-4B BaseRTQ4	91.3	105.6	0.87×	2,822	3,378	0.84×	1 / 2
Qwen3-4B-Instruct-2507 BaseRTQ4	73.7	89.9	0.82×	11,246	3,345	3.36×	1 / 2
Qwen3-4B-Instruct-2507 BaseRTQ8	54.8	63.5	0.86×	10,970	3,402	3.22×	1 / 2
gpt-oss-20b BaseRTQ8	51.4	100.6	0.51×	4,421	1,065	4.15×	1 / 1
gpt-oss-20b BaseRTmxfp4	60.2	103.6	0.58×	4,424	1,013	4.37×	1 / 1
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 BaseRTQ4	99.8	100.1	1.00×	3,862	2,499	1.55×	1 / 1
Qwen3-30B-A3B-Thinking-2507 BaseRTQ4	81.1	104.4	0.78×	4,738	3,694	1.28×	1 / 1
Qwen3-4B-Thinking-2507 BaseRTQ4	73.2	91.6	0.80×	11,020	3,363	3.28×	1 / 1
Qwen3-4B-Thinking-2507 BaseRTQ8	54.7	64.7	0.84×	11,473	3,414	3.36×	1 / 1
Qwen3.5-2B-Base BaseRTQ4	181.9	218.2	0.83×	4,191	2,645	1.58×	1 / 1

Side A · NVIDIA GB10

Top 25 of 71 runs by decode throughput.

Model / format	Chip / backend	Decode	Prefill	Contributor	Date	Report
Qwen3-0.6B-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	458.8 TG128 @ 1 ctx	20,970 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3-0.6B-cuda-q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	456.1 TG128 @ 1 ctx	52,750 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3-0.6B-cuda-q4mix BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	413.2 TG128 @ 1 ctx	49,865 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3-0.6B-cuda-q8 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	315.8 TG128 @ 1 ctx	51,373 PP512	basecompute	19 Sep 2026	View Benchmark
gemma-3-1b-it-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	295.9 TG128 @ 1 ctx	14,350 PP512	basecompute	19 Sep 2026	View Benchmark
gemma-3-1b-it-cuda-q4mix BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	268.0 TG128 @ 1 ctx	33,841 PP512	basecompute	19 Sep 2026	View Benchmark
Llama-3.2-1B-Instruct-cuda-q4mix BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	261.3 TG128 @ 1 ctx	36,647 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3-1.7B-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	202.5 TG128 @ 1 ctx	7,394 PP512	basecompute	19 Sep 2026	View Benchmark
Llama-3.2-1B-Instruct-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	195.3 TG128 @ 1 ctx	33,397 PP512	basecompute	19 Sep 2026	View Benchmark
gemma-3-1b-it-cuda-q8 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	193.2 TG128 @ 1 ctx	33,641 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-2B-Base-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	181.9 TG128 @ 1 ctx	4,191 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-2B-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	181.6 TG128 @ 1 ctx	4,185 PP512	basecompute	19 Sep 2026	View Benchmark
Llama-3.2-1B-Instruct-cuda-q8 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	173.7 TG128 @ 1 ctx	34,296 PP512	basecompute	19 Sep 2026	View Benchmark
Llama-3.2-1B-Instruct-Q8 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	172.8 TG128 @ 1 ctx	35,941 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-2B-cuda-q4mix BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	151.5 TG128 @ 1 ctx	6,837 PP512	basecompute	19 Sep 2026	View Benchmark
gemma-4-E2B-it-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	139.8 TG128 @ 1 ctx	12,036 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-2B-cuda-q8 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	113.5 TG128 @ 1 ctx	6,754 PP512	basecompute	19 Sep 2026	View Benchmark
Llama-3.2-3B-Instruct-cuda-q4mix BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	110.5 TG128 @ 1 ctx	14,651 PP512	basecompute	19 Sep 2026	View Benchmark
NVIDIA-Nemotron-3-Nano-30B-A3B-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	99.8 TG128 @ 1 ctx	3,862 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3-30B-A3B-Instruct-2507-cuda-q4mix BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	99.0 TG128 @ 1 ctx	7,439 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-35B-A3B-cuda-q4mix BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	92.4 TG128 @ 1 ctx	2,521 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3-4B-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	91.3 TG128 @ 1 ctx	2,822 PP512	basecompute	19 Sep 2026	View Benchmark
Llama-3.2-3B-Instruct-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	89.7 TG128 @ 1 ctx	14,073 PP512	basecompute	19 Sep 2026	View Benchmark
gemma-4-E2B-it-cuda-q8 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	87.7 TG128 @ 1 ctx	19,246 PP512	basecompute	19 Sep 2026	View Benchmark
Qwen3.5-35B-A3B-Q4 BaseRT 0.2.6Q4	NVIDIA GB10 CUDA	84.4 TG128 @ 1 ctx	2,093 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