Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite) vs Apple M4 (10-core GPU, 120 GB/s)
No like-for-like comparison yet: both have published results, but model size or batch differ. Treat the numbers below as indicative only.
| Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite) | Apple M4 (10-core GPU, 120 GB/s) | |
|---|---|---|
| Best image classification | 182 FPS D undated | 417 FPS B 2026 (year only) |
| Power | n/a | n/a |
| Perf / W | n/a | n/a |
| Price | n/a | n/a |
| Workloads with data | 1 of 7 | 6 of 7 |
Best published image classification result per platform. n/a = not published; never estimated.
Show data — all workloads, sources and like-for-like groups
object detection
Only Apple M4 (10-core GPU, 120 GB/s) has published object detection results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Apple M4 (10-core GPU, 120 GB/s) | 357 FPS latency_e2e · b=1 | YOLO26n FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Apple Core AI integration benchmark 2026 (year only) |
Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite):
No published object detection results for Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite) or Apple M4 (10-core GPU, 120 GB/s) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
instance segmentation
Only Apple M4 (10-core GPU, 120 GB/s) has published instance segmentation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Apple M4 (10-core GPU, 120 GB/s) | 196 FPS latency_e2e · b=1 | YOLO26n-seg FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Apple Core AI integration benchmark 2026 (year only) |
Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite):
No published instance segmentation results for Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite) or Apple M4 (10-core GPU, 120 GB/s) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
pose estimation
Only Apple M4 (10-core GPU, 120 GB/s) has published pose estimation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Apple M4 (10-core GPU, 120 GB/s) | 370 FPS latency_e2e · b=1 | YOLO26n-pose FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Apple Core AI integration benchmark 2026 (year only) |
Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite):
No published pose estimation results for Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite) or Apple M4 (10-core GPU, 120 GB/s) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
image classification
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Apple M4 (10-core GPU, 120 GB/s) | 417 FPS latency_e2e · b=1 | YOLO26n-cls FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Apple Core AI integration benchmark 2026 (year only) |
| Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite) MIXED: model-size class mid-size CNN vs nano (YOLO-n) | 182 FPS throughput · b=1 | ResNet18 (PyTorch) not stated in table · FPGA AI Suite on-chip parameter streaming; IP throughput · mid-size CNN | D | unknown power n/a no power published | Altera FPGA AI Suite Handbook (performance tables) undated |
No shared like-for-like group exists for this pair.
semantic segmentation
Only Apple M4 (10-core GPU, 120 GB/s) has published semantic segmentation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Apple M4 (10-core GPU, 120 GB/s) | 53.48 FPS latency_e2e · b=1 | YOLO26n-sem FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Apple Core AI integration benchmark 2026 (year only) |
Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite):
No published semantic segmentation results for Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite) or Apple M4 (10-core GPU, 120 GB/s) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
speech-to-text
No published speech-to-text results for Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite) or Apple M4 (10-core GPU, 120 GB/s) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
LLM generation
Only Apple M4 (10-core GPU, 120 GB/s) has published LLM generation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Apple M4 (10-core GPU, 120 GB/s) | 24.11 tok/s tg128 · b=1 | Llama 2 7B Q4_0 · llama.cpp Metal (commit 8e672ef) · LLM 5–14B | C | unknown power n/a no power published | llama.cpp discussion #4167 scoreboard (via GitHub API) 2026-10-01 |
Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite):
No published LLM generation results for Altera Agilex 7 FPGA I-Series Development Kit (FPGA AI Suite) or Apple M4 (10-core GPU, 120 GB/s) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
How to read these numbers
Machine-readable: /find.json (see llms.txt); full tables are in “Show data”. Confidence grades: A audited MLPerf closed · B documented third-party · C community · D vendor-published · E estimate. Unknown values are shown as unknown, never estimated. Generated 2026-10-16 from public sources.