- Gemma 4 E2B Excalidraw demo hits 35+ tokens/s on Chrome 134+ WebGPU.
- TurboQuant compresses KV cache 2.4× for browser fit.
- Generates 50-token code vs. 5,000-token JSON; needs 3 GB RAM.
Teamchong released the Gemma 4 E2B Excalidraw demo in October 2024. It runs client-side in Chrome 134+ via WebGPU. Benchmarks on Teamchong's GitHub repository show 35+ tokens/s speeds. TurboQuant provides 2.4× KV cache compression.
Excalidraw creates hand-drawn vector diagrams for scatter plots and flowcharts. This demo removes cloud needs. Financial analysts prototype BI dashboards fast.
Try the demo. It uses WGSL compute shaders for GPU speed.
Gemma 4 E2B Demo Turns Prompts into Compact Code
Gemma 4 E2B converts text prompts to ~50-token Excalidraw code. Users describe bar charts, Sankey diagrams, or candlesticks. Teamchong's repository notes this skips 5,000-token JSON.
Excalidraw renders editable visuals client-side. No API latency slows work. Quant teams sketch volatility surfaces on laptops.
Turboquant-wasm offers WASM+SIMD for CPU fallback, per Teamchong's repo.
Hardware Needs Drive 35 Tokens/s Performance
Chrome 134+ supports WebGPU subgroups, states Google's developer docs. Tests require 3 GB RAM for model weights and KV cache.
TurboQuant uses polar quantization and QJL adaptation for 2.4× compression, per Teamchong benchmarks. Chrome WebGPU docs.
StatCounter data from September 2024 shows Chrome at 82.5% enterprise desktop share.
TurboQuant KV Compression Speeds Inference
TurboQuant shrinks KV caches in Gemma 4 E2B layers. Polar quantization encodes magnitudes by angles. QJL adds low-rank matrices for 2.4× reduction, per Teamchong benchmarks.
WGSL shaders hit 35+ tokens/s on integrated GPUs. Uncompressed caches crash browsers.
Tableau and Power BI could adopt similar runtimes. Analysts build revenue funnels in seconds.
- Metric: KV Cache Size · Uncompressed: 5,000 tokens · TurboQuant: 2,000 tokens
- Metric: Inference Speed · Uncompressed: <10 tok/s · TurboQuant: 35+ tok/s
- Metric: Output Format · Uncompressed: Full JSON · TurboQuant: Compact Code
Data from Teamchong's GitHub tests on Chrome 134 with 3 GB RAM.
Financial Data Viz Gains from Browser AI
Quant finance needs precise diagrams. Gemma 4 E2B generates ML pipeline graphs and portfolio Sankeys. Client-side cuts cloud costs.
Excalidraw follows Stephen Few's clarity rules with sparse elements. Pair with D3.js for stock lines or Plotly surfaces.
Chrome's 82.5% share, per StatCounter September 2024, aids adoption.
Browser AI Reshapes Quant Platforms
BI tools seek zero-latency rendering. Gemma 4 E2B speeds diagram analytics.
Open-source TurboQuant draws vendors. WebGPU supports Gemma models, per Google Gemma docs.
Finance embeds on-device AI for real-time market visuals. Teams build Tufte-style tools faster.
Frequently Asked Questions
What is the Gemma 4 E2B Excalidraw demo?
Teamchong's browser demo runs Gemma 4 E2B to create Excalidraw diagrams from prompts using TurboQuant's 2.4× KV compression. Outputs average 50 tokens at 35+ tok/s.
What specs support the Gemma 4 E2B Excalidraw demo?
Desktop Chrome 134+ with WebGPU subgroups and 3 GB RAM. Achieves 35+ tokens/s via WGSL shaders. CPU fallback via turboquant-wasm.
How does TurboQuant enhance the Gemma 4 E2B demo?
Compresses KV cache 2.4× using polar quantization and QJL. Powers 35+ tok/s GPU inference in browser environments.
Do enterprises benefit from browser AI diagrams?
Yes. Server-free Excalidraw generation aligns with Tufte and Few principles for BI tools like Tableau and Power BI.
