Apple M4 (10-core GPU, 120 GB/s) vs Raspberry Pi 5 (CPU only)
Apple M4 (10-core GPU, 120 GB/s) is faster in all 5 like-for-like comparisons (largest gap: semantic segmentation, 44.57×).
| Apple M4 (10-core GPU, 120 GB/s) | Raspberry Pi 5 (CPU only) | |
|---|---|---|
| Best object detection | 357 FPS B 2026 (year only) | 14.92 FPS B undated |
| Power | n/a | n/a |
| Perf / W | n/a | n/a |
| Price | n/a | n/a |
| Workloads with data | 6 of 7 | 4 of 7 |
Best published object detection result per platform (may be from different like-for-like groups — see data). n/a = not published; never estimated.
Show data — all workloads, sources and like-for-like groups
object detection
| 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) |
| Raspberry Pi 5 (CPU only) CPU-only | 14.92 FPS inference_time · b=1 | YOLO26n FP32/default · Ultralytics NCNN · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs benchmark table undated |
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) |
Raspberry Pi 5 (CPU only):
No published instance segmentation results for Raspberry Pi 5 (CPU only) 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) |
Raspberry Pi 5 (CPU only):
No published pose estimation results for Raspberry Pi 5 (CPU only) 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) |
| Raspberry Pi 5 (CPU only) CPU-only | 29.59 FPS inference_time · b=1 | YOLO26n-cls FP32 · Ultralytics PyTorch (CPU) · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Hailo integration benchmark 2026 (year only) |
semantic segmentation
| 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) |
| Raspberry Pi 5 (CPU only) CPU-only | 1.20 FPS inference_time · b=1 | YOLO26n-sem FP32 · Ultralytics PyTorch (CPU) · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Hailo integration benchmark 2026 (year only) |
speech-to-text
No published speech-to-text results for Apple M4 (10-core GPU, 120 GB/s) or Raspberry Pi 5 (CPU only) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
LLM generation
| 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 |
| Raspberry Pi 5 (CPU only) CPU-only | 1.20 tok/s generation_throughput · b=1 | DeepSeek R1 14b as in linked issue (Ollama default tags are 4-bit) · Ollama / llama.cpp (see linked issue) · LLM 5–14B | C | 13.00 W measured reported in result source — measured | Jeff Geerling ai-benchmarks README (Findings tables) undated |
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Apple M4 (10-core GPU, 120 GB/s) | 41.31 tok/s generation_throughput · b=1 | Llama 3.2:3b as in linked issue (Ollama default tags are 4-bit) · Ollama / llama.cpp (see linked issue) · LLM ≤4B | C | 30.10 W measured reported in result source — measured | Jeff Geerling ai-benchmarks README (Findings tables) undated |
| Raspberry Pi 5 (CPU only) CPU-only | 4.88 tok/s generation_throughput · b=1 | Llama 3.2:3b as in linked issue (Ollama default tags are 4-bit) · Ollama / llama.cpp (see linked issue) · LLM ≤4B | C | 11.90 W measured reported in result source — measured | Jeff Geerling ai-benchmarks README (Findings tables) undated |
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.