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▶ 18:38 · $AMD · General Compute CEO: AMD inference deals with Anthropic, Meta, OpenAI make obvious sense on TCO
episode briefing
The Information

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2026-07-23 · 5 company · 4 thematic
sentiment
2 bull2 bear1 neu
speakers
jason goodison

Leads General Compute, a neocloud provider building inference infrastructure on SambaNova ASICs instead of Nvidia GPUs; previously evaluated Cerebras, Groq, and other ASIC vendors.

quote
“we've already started to see deals like this happen, right? So, we have Meta that's doing a deal uh with AMD already. We've got OpenAI that's doing a deal with AMD and now kind of more of the same. I think the reason is because and Nvidia…”
—jason goodison
now playing · $AMD
$AMD···bullish· mediumjason goodison
General Compute CEO: AMD inference deals with Anthropic, Meta, OpenAI make obvious sense on TCO
AMD is neck-and-neck with Nvidia on inference performance while offering lower total cost of ownership, making deals with Anthropic, Meta, and OpenAI economically rational.
$SAMBA-NOVAbullish· high· posjason goodison
General Compute bets company on SambaNova SN50: 5x faster inference, 10T params per rack, 20kW draw
SambaNova's SN50 chip enables 1-2,000 tokens/sec on frontier models with 10T parameters on a single air-cooled 20kW rack, delivering the speed and efficiency General Compute needs…
$CBRS···neutral· mediumjason goodison
General Compute CEO: Cerebras fast but model size limited, cost explodes at scale
Cerebras achieves exceptional inference speed but hits a wall on large model scaling where costs become prohibitive, making it unsuitable for General Compute's frontier model targ…
$NVDA···bearish· highjason goodison
General Compute CEO rejects Nvidia for inference: margins too high, architecture wrong for fast tokens
Nvidia's healthy margins and circular financing deals subsidize demand but its Vera Rubin/Groq architecture requires 80 back-and-forth trips per token for large models, making it…
$GROQbearish· highjason goodison
General Compute CEO: Groq architecture fundamentally flawed for large models, 80 hops per token
Groq's attention-FFN disaggregation forces 80 round-trips per token on 80-layer models, creating insurmountable latency for large model inference despite fast single-chip performa…