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Eug

Lifer
Mar 11, 2000
23,739
1,271
126
M1
5 nm
Unified memory architecture - LP-DDR4
16 billion transistors

8-core CPU

4 high-performance cores
192 KB instruction cache
128 KB data cache
Shared 12 MB L2 cache

4 high-efficiency cores
128 KB instruction cache
64 KB data cache
Shared 4 MB L2 cache
(Apple claims the 4 high-effiency cores alone perform like a dual-core Intel MacBook Air)

8-core iGPU (but there is a 7-core variant, likely with one inactive core)
128 execution units
Up to 24576 concurrent threads
2.6 Teraflops
82 Gigatexels/s
41 gigapixels/s

16-core neural engine
Secure Enclave
USB 4

Products:
$999 ($899 edu) 13" MacBook Air (fanless) - 18 hour video playback battery life
$699 Mac mini (with fan)
$1299 ($1199 edu) 13" MacBook Pro (with fan) - 20 hour video playback battery life

Memory options 8 GB and 16 GB. No 32 GB option (unless you go Intel).

It should be noted that the M1 chip in these three Macs is the same (aside from GPU core number). Basically, Apple is taking the same approach which these chips as they do the iPhones and iPads. Just one SKU (excluding the X variants), which is the same across all iDevices (aside from maybe slight clock speed differences occasionally).

EDIT:



M1 Pro 8-core CPU (6+2), 14-core GPU
M1 Pro 10-core CPU (8+2), 14-core GPU
M1 Pro 10-core CPU (8+2), 16-core GPU
M1 Max 10-core CPU (8+2), 24-core GPU
M1 Max 10-core CPU (8+2), 32-core GPU

M1 Pro and M1 Max discussion here:

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M1 Ultra discussion here:

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M2 discussion here:

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Second Generation 5 nm
Unified memory architecture - LPDDR5, up to 24 GB and 100 GB/s
20 billion transistors

8-core CPU

4 high-performance cores
192 KB instruction cache
128 KB data cache
Shared 16 MB L2 cache

4 high-efficiency cores
128 KB instruction cache
64 KB data cache
Shared 4 MB L2 cache

10-core iGPU (but there is an 8-core variant)
3.6 Teraflops

16-core neural engine
Secure Enclave
USB 4

Hardware acceleration for 8K h.264, h.264, ProRes

M3 Family discussion here:

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M4 Family discussion here:

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Last edited:
Reactions: Nothingness, Nosware, Charolette-Yog and 7 others
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Nothingness

Platinum Member
Jul 3, 2013
2,712
1,330
136
roger_k said:
I don't think that dealing with outliers is the biggest challenge. Scraping the results is probably the most annoying part IMO.
Click to expand...
Indeed the DB has become huge with more than 6M entries. And I guess their anti DDoS front-end would likely ban anyone trying to download too many results.
 
Upvote 0 Downvote
R

roger_k

Member
Sep 23, 2021
102
215
86
Nothingness said:
Indeed the DB has become huge with more than 6M entries. And I guess their anti DDoS front-end would likely ban anyone trying to download too many results.
Click to expand...

Pretty much this. I did scrape a few thousands results some years ago, and it was a drag. Luckily, they've you the possibility to access results as neatly formatted JSON, but you need to login to do that, which makes it hard to automate. I'd probably need to write a browser extension for this. If I ever find time.
 
Reactions: Tlh97 and igor_kavinski
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Gideon

Golden Member
Nov 27, 2007
1,703
3,911
136
igor_kavinski said:
I see in the GB command line that it captures memory timings. Is that included in the formatted JSON after login?
Click to expand...
Geekbench 4 used to have a memory-latency test even (that gave some points):

Micro-Star International Co., Ltd. MS-7A34 - Geekbench

Benchmark results for a Micro-Star International Co., Ltd. MS-7A34 with an AMD Ryzen 7 3700X processor.
browser.geekbench.com

but the removed it in v5. I Guess they still measure it but it isn't visible in their json dump. Here is the output of a random gb6 run:

Create JSON or Store JSON files for free online using JSONBin.io

Create JSON or Store JSON files for free online using JSONBin.io
jsonbin.io

(link will expire in 24h, didn't find a good place to upload json for longer)
 
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I

igor_kavinski

Lifer
Jul 27, 2020
17,702
11,465
106
Reproducing here in case anyone else wants to look later:

Code: 
{
  "date": "2024-05-14 04:19:56 +0200",
  "document_type": 0,
  "document_version": 6,
  "branch": "rosedale-main-build",
  "browser": {
    "url": "https://browser.geekbench.com/v6/cpu/6088523"
  },
  "build": 603408,
  "checksum": "c3b7b9d819736b8fbe52df821ae1a3cdbee7d139",
  "commit": "bca065a7d9",
  "options": {
    "iterations": 5,
    "cpu_workers": 0,
    "memory_workers": 0,
    "workload_gap": 5000
  },
  "platform": {
    "os": "iOS",
    "architecture": "aarch64",
    "bits": 64
  },
  "processor_frequency": {
    "frequencies": [
      3215,
      3206,
      3202,
      3197,
      3207,
      3206,
      3204,
      3212,
      3201,
      3208,
      3208,
      3207,
      3208,
      3208,
      3204,
      3217,
      3202,
      3218,
      3212,
      3217,
      3175,
      3206,
      3214,
      3212,
      3206,
      3215,
      3212,
      3216,
      3210,
      3214,
      3211,
      3209,
      3216
    ]
  },
  "runtime": 296.7316485,
  "system_uuid": "32A5276D-3E55-4FB2-A9F4-30B0D126BCFE",
  "clock": 560.4642663333334,
  "uuid": "84d7421c4a5740a8916463dbbb0a64ec",
  "version": "Geekbench 6.3.0",
  "metrics": [
    {
      "id": 1,
      "value": "iOS AArch64",
      "ivalue": 0
    },
    {
      "id": 2,
      "value": "Clang 16.0.0 ",
      "ivalue": 0
    },
    {
      "id": 3,
      "value": "iOS 17.4.1",
      "ivalue": 0
    },
    {
      "id": 4,
      "value": "iPhone14,4",
      "ivalue": 0
    },
    {
      "id": 5,
      "value": "iPhone 13 mini",
      "ivalue": 0
    },
    {
      "id": 6,
      "value": "D16AP",
      "ivalue": 0
    },
    {
      "id": 7,
      "value": "ARM",
      "ivalue": 0
    },
    {
      "id": 8,
      "value": "Apple A15 Bionic",
      "ivalue": 0
    },
    {
      "id": 9,
      "value": "Apple A15 Bionic",
      "ivalue": 0
    },
    {
      "id": 12,
      "value": "6",
      "ivalue": 6
    },
    {
      "id": 13,
      "value": "6",
      "ivalue": 6
    },
    {
      "id": 14,
      "value": "1",
      "ivalue": 1
    },
    {
      "id": 15,
      "value": "3.23 GHz",
      "ivalue": 3230000000
    },
    {
      "id": 17,
      "value": "128 KB",
      "ivalue": 131072
    },
    {
      "id": 18,
      "value": "1",
      "ivalue": 1
    },
    {
      "id": 19,
      "value": "64.0 KB",
      "ivalue": 65536
    },
    {
      "id": 20,
      "value": "1",
      "ivalue": 1
    },
    {
      "id": 21,
      "value": "4.00 MB",
      "ivalue": 4194304
    },
    {
      "id": 22,
      "value": "1",
      "ivalue": 1
    },
    {
      "id": 23,
      "value": "0.00 B",
      "ivalue": 0
    },
    {
      "id": 24,
      "value": "1",
      "ivalue": 1
    },
    {
      "id": 25,
      "value": "0.00 B",
      "ivalue": 0
    },
    {
      "id": 26,
      "value": "1",
      "ivalue": 1
    },
    {
      "id": 27,
      "value": "0.00 Hz",
      "ivalue": 0
    },
    {
      "id": 29,
      "value": "3.55 GB",
      "ivalue": 3812229120
    },
    {
      "id": 30,
      "value": "",
      "ivalue": 0
    },
    {
      "id": 40,
      "value": "0x1",
      "ivalue": 1
    },
    {
      "id": 45,
      "value": "2",
      "ivalue": 2
    },
    {
      "id": 46,
      "value": "2 Cores",
      "ivalue": 0
    },
    {
      "id": 47,
      "value": "2",
      "ivalue": 2
    },
    {
      "id": 51,
      "value": "4 Cores",
      "ivalue": 0
    },
    {
      "id": 52,
      "value": "4",
      "ivalue": 4
    },
    {
      "id": 71,
      "value": "1080",
      "ivalue": 1080
    },
    {
      "id": 72,
      "value": "2340",
      "ivalue": 2340
    },
    {
      "id": 73,
      "value": "3",
      "ivalue": 3
    },
    {
      "id": 74,
      "value": "",
      "ivalue": 0
    },
    {
      "id": 88,
      "value": "Clang",
      "ivalue": 0
    },
    {
      "id": 89,
      "value": "3230 MHz",
      "ivalue": 3230
    },
    {
      "id": 90,
      "value": "",
      "ivalue": 0
    },
    {
      "id": 93,
      "value": "2",
      "ivalue": 2
    },
    {
      "id": 94,
      "value": "0x2",
      "ivalue": 2
    },
    {
      "id": 99,
      "value": "4",
      "ivalue": 4
    },
    {
      "id": 100,
      "value": "0x1",
      "ivalue": 1
    },
    {
      "id": 117,
      "value": "False",
      "ivalue": 0
    },
    {
      "id": 84,
      "value": "",
      "ivalue": 296731
    },
    {
      "id": 85,
      "value": "",
      "ivalue": 297000
    },
    {
      "id": 86,
      "value": "",
      "ivalue": 0
    }
  ],
  "multicore_score": 5560,
  "score": 2284,
  "valid": 1,
  "sections": [
    {
      "name": "Single-Core",
      "id": 1,
      "score": 2284,
      "valid": 1,
      "workloads": [
        {
          "name": "File Compression",
          "id": 101,
          "units": 2,
          "threads": 1,
          "score": 2390,
          "factory_clock": 562.0059890416667,
          "factory_runtime": 0.10972879166666667,
          "workload_clock": 562.1157325416667,
          "runtime": 1.7380773020833336,
          "runtime_warmup": 1.8162114166666667,
          "runtimes": [
            1.7375549583333334,
            1.7371022500000002,
            1.745300625,
            1.7323513750000001
          ],
          "valid": 1,
          "rate": 359856951.8457539,
          "work": 625459200,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "Navigation",
          "id": 102,
          "units": 26,
          "threads": 1,
          "score": 2306,
          "factory_clock": 575.8913233333334,
          "factory_runtime": 0.00004545833333333334,
          "workload_clock": 575.8914562083334,
          "runtime": 1.7272222395833334,
          "runtime_warmup": 1.7237690416666667,
          "runtimes": [
            1.7294637916666669,
            1.7206675833333334,
            1.7272422916666668,
            1.7315152916666667
          ],
          "valid": 1,
          "rate": 13.895142993173618,
          "work": 24,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "HTML5 Browser",
          "id": 103,
          "units": 25,
          "threads": 1,
          "score": 2263,
          "factory_clock": 589.7939984166667,
          "factory_runtime": 0.031746083333333334,
          "workload_clock": 589.8257689583334,
          "runtime": 0.17265488541666668,
          "runtime_warmup": 0.24447612500000002,
          "runtimes": [
            0.1735825,
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            0.17213354166666667,
            0.17244966666666667
          ],
          "valid": 1,
          "rate": 46.33520783784173,
          "work": 8,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "PDF Renderer",
          "id": 104,
          "units": 9,
          "threads": 1,
          "score": 2499,
          "factory_clock": 595.8025291250001,
          "factory_runtime": 0.011253791666666667,
          "workload_clock": 595.8138489583333,
          "runtime": 0.6058929791666666,
          "runtime_warmup": 0.6611766666666667,
          "runtimes": [
            0.6054234583333333,
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          ],
          "valid": 1,
          "rate": 57639288.12648191,
          "work": 34923240,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "Photo Library",
          "id": 105,
          "units": 20,
          "threads": 1,
          "score": 1987,
          "factory_clock": 603.9055857916667,
          "factory_runtime": 0.06758,
          "workload_clock": 603.973188625,
          "runtime": 0.5932385625000001,
          "runtime_warmup": 0.6132227916666667,
          "runtimes": [
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          ],
          "valid": 1,
          "rate": 26.970600044227567,
          "work": 16,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "Clang",
          "id": 201,
          "units": 23,
          "threads": 1,
          "score": 2977,
          "factory_clock": 612.9713435416667,
          "factory_runtime": 0.02183429166666667,
          "workload_clock": 612.9932132916667,
          "runtime": 0.5333346041666667,
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          "runtimes": [
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          ],
          "valid": 1,
          "rate": 14662.465062094969,
          "work": 7820,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "Text Processing",
          "id": 202,
          "units": 25,
          "threads": 1,
          "score": 2144,
          "factory_clock": 620.7332312083333,
          "factory_runtime": 0.0787835,
          "workload_clock": 620.8120236666667,
          "runtime": 1.1125918125,
          "runtime_warmup": 1.0617550833333333,
          "runtimes": [
            1.0870794166666666,
            1.1056485833333334,
            1.1212640833333334,
            1.1363751666666668
          ],
          "valid": 1,
          "rate": 171.6712255600928,
          "work": 191,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "Asset Compression",
          "id": 203,
          "units": 2,
          "threads": 1,
          "score": 2219,
          "factory_clock": 631.4665886666667,
          "factory_runtime": 0.07058408333333334,
          "workload_clock": 631.537189625,
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          "runtimes": [
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          ],
          "valid": 1,
          "rate": 72089492.73741122,
          "work": 140318280,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "Object Detection",
          "id": 301,
          "units": 20,
          "threads": 1,
          "score": 1807,
          "factory_clock": 646.22525825,
          "factory_runtime": 0.11048929166666667,
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          "runtime": 0.2958348645833333,
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          "valid": 1,
          "rate": 54.084227099247066,
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          "kernels": [],
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          "thermal_state": 0,
          "optimize_runtime": 0,
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        },
        {
          "name": "Background Blur",
          "id": 302,
          "units": 20,
          "threads": 1,
          "score": 1966,
          "factory_clock": 652.85802175,
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          "valid": 1,
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        },
        {
          "name": "Horizon Detection",
          "id": 401,
          "units": 9,
          "threads": 1,
          "score": 2726,
          "factory_clock": 664.0361865,
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          ],
          "valid": 1,
          "rate": 84813019.59362967,
          "work": 48000000,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "Object Remover",
          "id": 402,
          "units": 9,
          "threads": 1,
          "score": 2841,
          "factory_clock": 672.167337125,
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          "runtimes": [
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          ],
          "valid": 1,
          "rate": 218415819.01428616,
          "work": 360923220,
          "kernels": [],
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          "thermal_state": 0,
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          "transfer_runtime": 0
        },
        {
          "name": "HDR",
          "id": 403,
          "units": 9,
          "threads": 1,
          "score": 2397,
          "factory_clock": 685.5544829583333,
          "factory_runtime": 0.017214375,
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          "runtime_warmup": 0.9590756250000001,
          "runtimes": [
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          ],
          "valid": 1,
          "rate": 70325665.93379351,
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          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
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          "transfer_runtime": 0
        },
        {
          "name": "Photo Filter",
          "id": 404,
          "units": 20,
          "threads": 1,
          "score": 2495,
          "factory_clock": 695.3240937500001,
          "factory_runtime": 0.0015761666666666667,
          "workload_clock": 695.3257358750001,
          "runtime": 0.40387051041666666,
          "runtime_warmup": 0.4184380416666667,
          "runtimes": [
            0.4270941666666667,
            0.39687295833333336,
            0.39666270833333334,
            0.39485220833333334
          ],
          "valid": 1,
          "rate": 24.760411424154643,
          "work": 10,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "Ray Tracer",
          "id": 501,
          "units": 9,
          "threads": 1,
          "score": 1809,
          "factory_clock": 702.923320125,
          "factory_runtime": 0.8227607916666667,
          "workload_clock": 703.7460935833334,
          "runtime": 0.5711367916666668,
          "runtime_warmup": 0.5766775416666667,
          "runtimes": [
            0.566212,
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            0.57430775,
            0.58094875
          ],
          "valid": 1,
          "rate": 1750894.0320266238,
          "work": 1000000,
          "kernels": [],
          "temperatures": [],
          "thermal_state": 0,
          "optimize_runtime": 0,
          "transfer_runtime": 0
        },
        {
          "name": "Structure from Motion",
          "id": 502,
          "units": 9,
          "threads": 1,
          "score": 2148,
          "factory_clock": 711.7098989583334,
          "factory_runtime": 0.17787183333333334,
          "workload_clock": 711.8877935416667,
          "runtime": 0.941190875,
          "runtime_warmup": 0.902652125,
          "runtimes": [
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}
 
Reactions: Tlh97, Nothingness and Gideon
Upvote 0 Downvote

Gideon

Golden Member
Nov 27, 2007
1,703
3,911
136
igor_kavinski said:
Is it possible to get JSON for a benchmark run for M4 done by someone else? Could you post it, if you don't mind please?
Click to expand...

This is the 4.4 Ghz 4001 point run:

iPad16,3 - Geekbench

Benchmark results for an iPad16,3 with an ARM processor.
browser.geekbench.com

JSON: 
{
  "date": "2024-05-15 09:25:22 +0800",
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  "document_version": 6,
  "branch": "rosedale-main-build",
  "build": 603408,
  "checksum": "836a6069d646beb2af4829b7aa6d6bb59178d162",
  "commit": "bca065a7d9",
  "options": {
    "iterations": 5,
    "cpu_workers": 0,
    "memory_workers": 0,
    "workload_gap": 5000
  },
  "platform": {
    "os": "iOS",
    "architecture": "aarch64",
    "bits": 64
  },
  "processor_frequency": {
    "frequencies": [
      4391,
      4382,
      4390,
      4403,
      4405,
      4401,
      4406,
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      4402,
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    ]
  },
  "runtime": 233.29856320833335,
  "system_uuid": "45744C80-A87A-4DC9-9E0E-FEEC41B1B3B1",
  "clock": 953.28430408333338,
  "uuid": "0cc220a298e64f8e85a558dbe4c74871",
  "version": "Geekbench 6.3.0",
  "metrics": [
    {
      "id": 1,
      "value": "iOS AArch64",
      "ivalue": 0
    },
    {
      "id": 2,
      "value": "Clang 16.0.0 ",
      "ivalue": 0
    },
    {
      "id": 3,
      "value": "iOS 17.5",
      "ivalue": 0
    },
    {
      "id": 4,
      "value": "iPad16,3",
      "ivalue": 0
    },
    {
      "id": 5,
      "value": "iPad16,3",
      "ivalue": 0
    },
    {
      "id": 6,
      "value": "J717AP",
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    },
    {
      "id": 7,
      "value": "ARM",
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    },
    {
      "id": 8,
      "value": "ARM",
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    },
    {
      "id": 9,
      "value": "ARM",
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    },
    {
      "id": 12,
      "value": "9",
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      "id": 13,
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    {
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    },
    {
      "id": 15,
      "value": "4.41 GHz",
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    },
    {
      "id": 18,
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    {
      "id": 19,
      "value": "64.0 KB",
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    },
    {
      "id": 20,
      "value": "1",
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    },
    {
      "id": 21,
      "value": "4.00 MB",
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    },
    {
      "id": 22,
      "value": "1",
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    },
    {
      "id": 23,
      "value": "0.00 B",
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    },
    {
      "id": 24,
      "value": "1",
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    },
    {
      "id": 25,
      "value": "0.00 B",
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    },
    {
      "id": 26,
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    },
    {
      "id": 27,
      "value": "0.00 Hz",
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    },
    {
      "id": 29,
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    },
    {
      "id": 30,
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      "ivalue": 0
    },
    {
      "id": 40,
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    },
    {
      "id": 45,
      "value": "2",
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    },
    {
      "id": 46,
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    },
    {
      "id": 47,
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    {
      "id": 51,
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  ],
  "complete_benchmark": 1
}


Here is another gb5 result

iPad16,4 - Geekbench

Benchmark results for an iPad16,4 with an ARM processor.
browser.geekbench.com


JSON: 
{
  "date": "2024-05-09 07:42:47 +0100",
  "document_type": 0,
  "document_version": 5,
  "branch": "corktown-master-build",
  "build": 503987,
  "checksum": "c9dd07519c8bc77d0633dc4016c5b731a9c5409a",
  "commit": "753d06cb27",
  "options": {
    "iterations": 5,
    "cpu_workers": 0,
    "memory_workers": 0,
    "workload_gap": 1000
  },
  "platform": {
    "os": "iOS",
    "architecture": "aarch64",
    "bits": 64
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}
 
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richardskrad

Member
Jun 28, 2022
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I have the M1, M2 and M3 iPad Pro’s and I can’t tell the difference between them when it comes to snappiness. The M3 is supposed to be 50% faster in single core than the M1 in GB6 but that speed increase is nowhere to be seen in actual daily use. Why are we even paying attention to benchmark numbers?
 
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Eug

Lifer
Mar 11, 2000
23,739
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richardskrad said:
I have the M1, M2 and M3 iPad Pro’s and I can’t tell the difference between them when it comes to snappiness. The M3 is supposed to be 50% faster in single core than the M1 in GB6 but that speed increase is nowhere to be seen in actual daily use. Why are we even paying attention to benchmark numbers?
Click to expand...
Because why not? And of course, the M4 will be in Macs within the year too.

BTW, I wonder what this new “dedicated display engine” in M4 entails.



It seems they built tandem OLED support right into the display engine.

PS. Since you brought up benchmarks again, I’ll take this opportunity to repost the GB 6 bench from the other thread.

iPad16,6 - Geekbench

Benchmark results for an iPad16,6 with an ARM processor.
browser.geekbench.com
 
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Eug

Lifer
Mar 11, 2000
23,739
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poke01 said:
It means that future MacBooks ie the 14” and 16” with M4 or later will easily support Tandem OLED.
Click to expand...
Yes, that’s a given, although some predict those won’t come until late 2025 / early 2026, so it could very well be M5 series.

As a Mac mini user though, I’m more interested in tandem OLED in a standalone display. If they do release one of those, will it also require M4 or later? Or will it have an on-board A18 with its own tandem OLED capable display engine?

Anyhow I was just wondering what the “dedicated display engine” consists of.
Tandem OLED is also coming to the rumoured 20.3” device. A lot of Apples future devices will use this tech, that includes iPhones.
Click to expand...
As for the 20” device that rumour is a bit iffy at this point. Projected release date is 2027, and supposed to be foldable device.
 
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Doug S

Platinum Member
Feb 8, 2020
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Eug said:
As a Mac mini user though, I’m more interested in tandem OLED in a standalone display. If they do release one of those, will it also require M4 or later? Or will it have an on-board A18 with its own tandem OLED capable display engine?
Click to expand...

Why would A18 include that display engine? AFAIK they aren't planning to use tandem OLED in phones. Studio displays start at well over $1000, and it is safe to assume a tandem OLED version would be even pricier, so over $2000 seems likely.

They currently include an iPhone SoC, so upgrading that to an M4 wouldn't represent even 1% of the price.
 
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Eug

Lifer
Mar 11, 2000
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Doug S said:
Why would A18 include that display engine? AFAIK they aren't planning to use tandem OLED in phones. Studio displays start at well over $1000, and it is safe to assume a tandem OLED version would be even pricier, so over $2000 seems likely.

They currently include an iPhone SoC, so upgrading that to an M4 wouldn't represent even 1% of the price.
Click to expand...
I won’t rule out the possibility of tandem OLED in iPhones 📱 in the few years as it already exists in Android phones.

However, I wonder just how much die space this tandem OLED display support takes up.
 
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SiliconFly

Golden Member
Mar 10, 2023
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richardskrad said:
I have the M1, M2 and M3 iPad Pro’s and I can’t tell the difference between them when it comes to snappiness. The M3 is supposed to be 50% faster in single core than the M1 in GB6 but that speed increase is nowhere to be seen in actual daily use. Why are we even paying attention to benchmark numbers?
Click to expand...

Eug said:
PS. Since you brought up benchmarks again, I’ll take this opportunity to repost the GB 6 bench from the other thread.

iPad16,6 - Geekbench

Benchmark results for an iPad16,6 with an ARM processor.
browser.geekbench.com
Click to expand...

Just to put things in context, the very high Geekbench 6 results posted by M4 is due to a SME "bug" in the Geekbench benchmark suite. The rest of the competition doesn't support that instruction set there by making the comparison totally meaningless. For real world comparison, GB6 actually fails to exercise balanced testing in the case of M4 (it's like using specialized Nvidia driver calls for specific workloads to accelerate specific graphic benchmark routines in Nvidia's favor while not putting similar specialized effort for the competition's workloads). Please see the link below:

https://twitter.com/x/status/1788360191471431919

The actual IPC increase in M4 is not that high as reported by many tech sites (see the results above). I think the actual performance increase is actually due to higher clocks and not due to any architectural IPC gains. M3 to M4 IPC increase is pretty much non existent if not for SME and higher clocks. In real world use case scenarios, people won't feel much of a performance difference (unless the app they're using is fully SME complaint).

In short, M4 isn't that massive a jump as they want us to believe. Not even close. And they're not far ahead of competition either. I believe when Zen5 & Arrow Lake come out, they'll not only easily close the gap, but may even exceed M4. And if anyone's expecting a massive overall performance increase from M3 to M4, I don't think it's in the cards (except for SME compatible workloads).
 
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poke01

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Mar 8, 2022
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SiliconFly said:
Just to put things in context, the very high Geekbench 6 results posted by M4 is due to a SME "bug" in the Geekbench benchmark suite. The rest of the competition doesn't support that instruction set there by making the comparison totally meaningless. For real world comparison, GB6 actually fails to exercise balanced testing in the case of M4 (it's like using specialized Nvidia driver calls for specific workloads to accelerate specific graphic benchmark routines in Nvidia's favor while it's not putting similar specialized effort for the competition's workloads). Please see the link below:

https://twitter.com/x/status/1788360191471431919

The actual IPC increase in M4 is not that high as reported by many tech sites (see the results above). I think the actual performance increase is actually due to higher clocks and not due to any architectural IPC gains. M3 to M4 IPC increase is pretty much non existent if not for SME and higher clocks. In real world use case scenarios, people won't feel much of a performance difference (unless the app they're using is fully SME complaint).

In short, M4 isn't that massive a jump as they want us to believe. Not even close. And they're not far ahead of competition either. I believe when Zen5 & Arrow Lake come out, they'll not only easily close the gap, but may even exceed M4. And if anyone's expecting a massive overall performance increase from M3 to M4, I don't think it's in the cards (except for SME compatible workloads).
Click to expand...
not this again, just wait for for end of year. When they release the Mac’s with these chips. We’d have proper benchmarks then.

But in short looking at gb5 it’s about a 5% IPC improvement.
 
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Eug

Lifer
Mar 11, 2000
23,739
1,271
126
SiliconFly said:
Just to put things in context, the very high Geekbench 6 results posted by M4 is due to a SME "bug" in the Geekbench benchmark suite. The rest of the competition doesn't support that instruction set there by making the comparison totally meaningless. For real world comparison, GB6 actually fails to exercise balanced testing in the case of M4 (it's like using specialized Nvidia driver calls for specific workloads to accelerate specific graphic benchmark routines in Nvidia's favor while it's not putting similar specialized effort for the competition's workloads). Please see the link below:

https://twitter.com/x/status/1788360191471431919

The actual IPC increase in M4 is not that high as reported by many tech sites (see the results above). I think the actual performance increase is actually due to higher clocks and not due to any architectural IPC gains. M3 to M4 IPC is pretty much non existent if not for SME and higher clocks. In real world use case scenarios, people won't feel much of a performance difference (unless the app they're using is fully SME complaint).

In short, M4 isn't that massive a jump as they want us to believe. Not even close. And they're not far ahead of competition either. I believe when Zen5 & Arrow Lake come out, they'll not only easily close the gap, but may even exceed M4. If anyone's expecting a massive overall performance increase from M3 to M4, I don't think it's in the cards.
Click to expand...

You're a week behind. It's not a bug, but a feature.

Anyhow, the uplift is about 3% per clock. That person you linked revised his estimate later to indicate 3%, and I got a similar number comparing reported Geekbench 5 results.



Plus, this is performance per clock in a fanless tablet of course. We don't know yet if M4 Max will run at the same clock speed.
 
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SiliconFly

Golden Member
Mar 10, 2023
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poke01 said:
not this again, just wait for for end of year. When they release the Mac’s with these chips. We’d have proper benchmarks then.

But in short looking at gb5 it’s about a 5% IPC improvement.
Click to expand...
Eug said:
You're a week behind. It's not a bug, but a feature.

Anyhow, the uplift is about 3% per clock. That person you linked revised his estimate later to indicate 3%, and I got a similar number comparing reported Geekbench 5 results.

Plus, this is performance per clock in a fanless tablet of course. We don't know yet if M4 Max will run at the same clock speed.
Click to expand...
All I'm saying is, M4 isn't trivial by any means. It's a great product. But not exactly an extraordinary jump from M3 (except for SME which isn't for everyone). And the competition isn't that behind either. There's a very good chance they may surpass M4 this fall.
 
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Eug

Lifer
Mar 11, 2000
23,739
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SiliconFly said:
All I'm saying is, M4 isn't trivial by any means. It's a great product. But not exactly an extraordinary jump from M3 (except for SME which isn't for everyone). And the competition isn't that behind either. There's a very good chance they may surpass M4 this fall.
Click to expand...
Well, as discussed here by others smarter than me, performance per clock isn't everything. Apple prioritizes low power usage, so the numbers reflect that to an extent. Again, this is a fanless device, and furthermore, it doesn't even have thermal paste or a finned heatsink in it. It's essentially using the case as the heatsink, with a some graphite paper and a copper plated Apple logo.

Also, we don't actually know how Apple will be using SME. Maybe it will be important for them. Again, I defer the experts since I am not an engineer, but I don't necessarily buy the argument that if others don't have it, it shouldn't count. I don't claim to understand how this will be used in actual code, but my understanding is that at least some forms object detection are very important on Macs, even on consumer Macs.
 
Last edited:
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Doug S

Platinum Member
Feb 8, 2020
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Eug said:
but I don't necessarily buy the argument that if others don't have it, it shouldn't count
Click to expand...

The people saying that are probably all for AVX512 counting in benchmarks despite ARM CPUs having only 128 bit wide NEON making for an unfair comparison where SIMD resources dominate.

If you don't like all that extra stuff, just look at clang results and ignore the rest. New instructions are not going to speed up clang/gcc, bigger caches and higher clock rates on their own don't have a lot of impact. If you want a way to compare progress on CPUs that's unaffected by architectural changes aside from increases in basic wheels on the ground performance, that's how you do it.
 
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