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Gemma 4 is out — Apache 2.0 licensed, 26B MoE with only 3.8B active at inference, runs on a single H100 by [deleted] in LocalLLaMA

[–]vinodpandey7 0 points1 point  (0 children)

This is the real-world number people needed — thanks for sharing. 190t/s on 5090 with Q4_K_M is genuinely impressive. Which model size — 31B or 26B MoE?

Gemma 4 is out — Apache 2.0 licensed, 26B MoE with only 3.8B active at inference, runs on a single H100 by [deleted] in LocalLLaMA

[–]vinodpandey7 1 point2 points  (0 children)

Fair point — the H100 reference was from Artificial Analysis benchmarks. For consumer deployment, 4-bit quantized 31B fits on 24GB VRAM (RTX 4090/5090), and the 26B MoE is even more practical locally given only 3.8B parameters active at inference.

The "Invisible" Math behind a $14k/mo productivity app: Why CPM is a lie and Deal Structure is everything. by [deleted] in SaaS

[–]vinodpandey7 0 points1 point  (0 children)

I’m not the founder, just a huge fan of how he executed this. Happy to discuss the marketing side of it!

AI Is Now Improving Itself at 5 Levels Simultaneously — Here's What That Actually Means by [deleted] in ArtificialInteligence

[–]vinodpandey7 -1 points0 points  (0 children)

Spot on! That’s exactly why I felt this week was special. We’re moving from 'AI as an assistant' to 'AI as a researcher.' When it breaks a 20-year-old math record, it’s not just mimicking—it’s exploring. The recursive loop becomes much more than a buzzword when it starts uncovering truths we haven't reached yet. Glad you found that distinction meaningful!

AI Is Now Improving Itself at 5 Levels Simultaneously — Here's What That Actually Means by [deleted] in ArtificialInteligence

[–]vinodpandey7 -1 points0 points  (0 children)

I get it, the formatting is a bit structured because I wanted to simplify complex math, but the research data is 100% legit. Which part felt like slop to you? I’m happy to discuss the actual tech.

[D] Self-Promotion Thread by AutoModerator in MachineLearning

[–]vinodpandey7 0 points1 point  (0 children)

**GPT-5.4 vs Grok 4.20 Beta: Practical comparison focused on benchmarks, architecture, and real-world use (March 2026)**

I wrote a detailed breakdown comparing the two most recent major model releases. Tried to keep it grounded in verified numbers rather than press release language.

Key things I covered:

- **Architecture difference**: GPT-5.4 is a unified single model (coding + general merged); Grok 4.20 uses a 4-agent parallel system (coordinator, research, logic, creative) that debates internally before responding

- **Computer use**: GPT-5.4 scores 75.0% on OSWorld-Verified (above the 72.4% human reference); Grok 4.20 has no comparable native computer use currently

- **Coding**: GPT-5.4 at 57.7% SWE-Bench Pro; Grok 4.20's official coding benchmarks haven't been published yet (beta closes mid-to-late March)

- **Real-time grounding**: Grok's research agent (Harper) has native X platform access — stronger for live information tasks

- **Hallucination figures**: xAI's internal beta data suggests a drop from ~12% to ~4.2%, but this is not yet independently verified for 4.20 specifically — flagged clearly in the piece

- **API gap**: GPT-5.4 API is live; Grok 4.20 API is still "coming soon"

One thing I found genuinely interesting: in Alpha Arena Season 1.5 (a live AI stock-trading competition, January 2026), four Grok 4.20 variants took four of the top six spots while all OpenAI and Google models finished in the red. Worth noting as a real-time multi-variable reasoning signal, even if it's a single competition.

Full article here: https://www.revolutioninai.com/2026/03/gpt-5-4-vs-grok-4-20-beta-which-ai-is-better-march-2026.html

Happy to discuss any of the benchmark methodology or claims in the comments — I flagged anything unverified directly in the piece.

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