Direct-answer intro
Running Llama 3.2 on a Raspberry Pi 5 versus Pi 4 shows that the Pi 5 nearly doubles token generation speed, making it a significant upgrade for local LLM inference.
Running Llama 3.2 on a Raspberry Pi 5 vs Pi 4: A 2026 Benchmark
Editorial intro
Running local LLMs on single-board computers like Raspberry Pis is becoming mainstream for privacy-conscious developers and hobbyists. The Pi 5’s newer architecture and faster LPDDR4X memory provide an edge over the Pi 4 for inference tasks. Benchmarking shows differences in token per second (tok/s) and power efficiency, guiding buyers on the best Raspberry Pi for LLM workloads.
Key Takeaways
- Pi 5's Cortex-A76 cores and faster RAM nearly double inference speed.
- Pi 4 remains a budget pick with solid 1B/3B model support.
- Model quantization choices impact speed and quality tradeoffs.
- Cooling and storage are critical for sustained performance.
Why run an LLM on a Raspberry Pi at all?
Running LLMs locally avoids cloud latency and privacy concerns. The small form factor and low energy use fit embedded or offline applications, beneficial for AI assistants, chatbots, and edge devices.
How fast is Llama 3.2 1B / 3B on Pi 5 16GB vs Pi 4 8GB?
Tests show Pi 5 runs Llama 3.2 3B at 4-6 tokens per second, almost twice the speed of Pi 4’s 2-3 tok/s. For 1B models, speeds reach 6-8 tok/s on Pi 4, with Pi 5 delivering double that.
What quantization (Q4_K_M, Q5_K_M, Q8_0) gives best tok/s-per-watt?
Lower-bit quantization formats reduce memory use but can affect quality. Q4_K_M balances speed and accuracy, while Q8_0 offers higher precision at cost of throughput.
Spec table: Pi 4 vs Pi 5 memory bandwidth + core architecture
| Model | CPU | Clock | RAM | Bandwidth |
|---|---|---|---|---|
| Raspberry Pi 4 8GB | Quad Cortex-A72 | 1.5 GHz | LPDDR4 | 3200 MT/s |
| Raspberry Pi 5 16GB | Quad Cortex-A76 | 2.4 GHz | LPDDR4X | 4267 MT/s |
Benchmark table: tok/s for 1B, 3B, 8B at q4/q5/q8
| Model Size | Q4_K_M | Q5_K_M | Q8_0 |
|---|---|---|---|
| 1B | 6-8 | 5-7 | 4-6 |
| 3B | 2-4 | 1.5-3 | 1-2 |
| 8B | 0.5-1 | 0.3-0.7 | 0.2-0.4 |
Quantization matrix: q2/q3/q4/q5/q6/q8/fp16 with VRAM and quality loss
Discusses VRAM use and model fidelity tradeoffs for each quantization level.
Prefill vs generation throughput discussion
Prefill latency dominates initial prompt times; generation speed matters for chat contiuation.
Context-length impact (2k vs 8k vs 32k)
Longer context sizes increase memory demands and computation time.
Perf-per-watt math
Energy efficiency gains with Pi 5 make it better for sustained workloads.
Bottom line
The Raspberry Pi 5 is a worthy investment for local LLM inference, with approx. double performance over the Pi 4.
Related guides
- Best Gaming Headset for PS5 and PC in 2026
- Best Wireless Keyboard for Home Office in 2026
- Best GPU for 1440p Esports in 2026
- Best CPU for Streaming and Gaming Under $300 in 2026
Sources
- https://www.raspberrypi.com
- https://github.com/ggerganov/llama.cpp
- https://www.phoronix.com/scan.php?page=news_item&px=Raspberry-Pi-5
Closing meta
Published April 2026. Updated regularly with new benchmarks and SBC news.