Mastering Edge AI on Raspberry Pi with LiteRT and Gemma
On a Raspberry Pi 5, LiteRT-LM delivers a robust performance for Gemma 4 E2B, achieving 99 tokens/sec for prefill and 9 tokens/sec for decode, all while maintaining a remarkably low peak memory footprint of just 1432 MB. In comparison, the integrated Broadcom VideoCore VII GPU is clocked at 800 MHz and offers a peak of ~76.8 GFLOPS (FP32) and ~0.24 TOPS (INT8).While the CPU possesses a massive capacity advantage, the GPU introduces heterogeneous parallel execution, a paradigm critical for real-time edge applications. Rather than saturating the CPU, developers can delegate tasks across both processors to optimize overall system and thermal efficiency.
- ▪On a Raspberry Pi 5, LiteRT-LM delivers a robust performance for Gemma 4 E2B, achieving 99 tokens/sec for prefill and 9 tokens/sec for decode, all while maintaining a remarkably low peak memory footprint of just 1432 MB.
- ▪In comparison, the integrated Broadcom VideoCore VII GPU is clocked at 800 MHz and offers a peak of ~76.8 GFLOPS (FP32) and ~0.24 TOPS (INT8).While the CPU possesses a massive capacity advantage, the GPU introduces heterogeneous parallel ex
- ▪Rather than saturating the CPU, developers can delegate tasks across both processors to optimize overall system and thermal efficiency.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,493 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Googleblog |
| Canonical URL | https://developers.googleblog.com/mastering-edge-ai-on-raspberry-pi-with-litert-and-gemma/ |
| Publication time | Tue, 11 Aug 2026 17:23:58 +0000 |
| Retrieval time | 2026-08-11T17:30:45.284Z |
| Last seen | 2026-08-11T17:30:45.284Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Vxv7uyCMrCCK · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
On a Raspberry Pi 5, LiteRT-LM delivers a robust performance for Gemma 4 E2B, achieving 99 tokens/sec for prefill and 9 tokens/sec for decode, all while maintaining a remarkably low peak memory footprint of just 1432 MB. This brings Gemma’s highly responsive, general-purpose intelligence to Raspberry Pi.Thanks to Gemma 4 E2B's highly efficient tokenizer, which packs more text into fewer tokens (averaging ~4.2 characters per token), LiteRT-LM achieves an impressive end-to-end generation speed of ~27.3 characters per sec, roughly 300 words per minute (wpm), in the Reachy Mini voice demo.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Googleblog.
