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No more waitlist – Cerebras inference API is open to all! 1M free tokens/day 20x GPU speed Reasoning in ~1 second It's time to build!

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Cerebras

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108,466 Aufrufe • vor 1 Jahr •via X (Twitter)

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Cerebras Code Plans are open for business and massively upgraded. Pro Plans ($50): 165k▶️300k TPM @ 24M Tokens/day Max Plans ($200): 300k▶️400k TPM @ 120M Tokens/day More vibing, more tokens. Keep sending us feedback!
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Cerebras Code Plans are open for business and massively upgraded. Pro Plans ($50): 165k▶️300k TPM @ 24M Tokens/day Max Plans ($200): 300k▶️400k TPM @ 120M Tokens/day More vibing, more tokens. Keep sending us feedback!

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Cerebras Code just got an UPGRADE. It's now powered by GLM 4.6 Pro Plans ($50): 300k ▶️ 1M TPM @ 24M Tokens/day Max Plans ($200): 400k ▶️ 1.5M TPM @ 120M Tokens/day Fastest GLM provider on the planet at 1000 tokens/s and at 131K context. Get yours before we run out 👇
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🟪 Qwen3-235B 2507 Instruct is live on Cerebras 1400 TPS • 131K Context • 230ms TTFT $0.6 | $1.2 per M tokens Try chat: Get API key: Pay-as-you-go via
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🟪 Qwen3-235B 2507 Instruct is live on Cerebras 1400 TPS • 131K Context • 230ms TTFT $0.6 | $1.2 per M tokens Try chat: Get API key: Pay-as-you-go via

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Dynamic Positions is live — unlocking positions across 1,400 bins. 20x cheaper. 20x more bins. 1-Position. There is no better time to learn how to DLMM.
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We put OpenAI's GPT-OSS on the Cerebras CS-4 and raced it against the competition. The task: build the full periodic table of elements. CS-4 finished in 4 seconds, hitting >4,000 tps The GPU took >1 minute 40 seconds. In AI, speed is productivity. And at Cerebras, we build the fastest AI in the world.
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🚀Announcement: Flap waitlist season 1🚀 1/ #JourneyBegins Flap Waitlist Season 1 is now live. Visit to join the waitlist. It's time for degens!! Register now for exclusive access to the alpha test and a chance to partake in future airdrops. 👇👇
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🚀Announcement: Flap waitlist season 1🚀 1/ #JourneyBegins Flap Waitlist Season 1 is now live. Visit to join the waitlist. It's time for degens!! Register now for exclusive access to the alpha test and a chance to partake in future airdrops. 👇👇

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69,199 Aufrufe • vor 2 Jahren

We built Death by Diet Coke in less than 30 seconds using Cerebras Code Pro, where you get higher rate limits and more power for Qwen3-Coder. We are opening the same number of Cerebras Code Pro/Max plans as diet coke cans in the office. First come, first serve.
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o1-pro now available in API ben hylak him Sahil Lavingia sam Kai adi Alec Velikanov olives Zoe MK@LLM Researcher @theemao christian It uses more compute than o1 to provide consistently better responses. Available to select developers on tiers 1–5. Supports vision, function calling, Structured Outputs, and works with the Responses and Batch APIs. With more compute, comes more cost: $150 / 1M input tokens and $600 / 1M output tokens.
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> 8 GPUs in one server rig > dude went homeless to build it > electrical bill costs more than rent now > while everyone else pays $400/month to openai > a 2 GPU desktop kills the api bill forever > rtx 4080 super + rtx 5060 ti = 32gb vram > runs qwen 3.6 with 100k context locally > no rate limits, no api keys, no data leaving the room > agents loop 400 times for free > claude opus still wins on hard reasoning > but local handles 90% of daily work > $1,200 setup pays itself off in 4 months > bookmark this and read the article below
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> 8 GPUs in one server rig > dude went homeless to build it > electrical bill costs more than rent now > while everyone else pays $400/month to openai > a 2 GPU desktop kills the api bill forever > rtx 4080 super + rtx 5060 ti = 32gb vram > runs qwen 3.6 with 100k context locally > no rate limits, no api keys, no data leaving the room > agents loop 400 times for free > claude opus still wins on hard reasoning > but local handles 90% of daily work > $1,200 setup pays itself off in 4 months > bookmark this and read the article below

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I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?
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I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

Alok

12,962 Aufrufe • vor 2 Monaten

China is literally on 🔥 Baidu from China has launched ERNIE 4.5 and ERNIE X1 and it’s freaking cheap . Here is everything you need to know. ERNIE 4.5 - Native multimodal and Outperforms GPT 4.5 in multiple benchmarks at just 1% of GPT 4.5 price - OpenAI GPT 4.5 – Input: $75 / 1M tokens, Output: $150 / 1M tokens; - ERNIE 4.5 – Input: $0.55 / 1M tokens, Output: $2.20 / 1M tokens ERNIE X1 - A deep thinking reasoning model with multimodal capabilities on par with DeepSeek R1 at only half the price See it in action and check out the pricing details👇 📹 source : yiyan[.]baidu[.]com 1/6 ERNIE 4.5 is a multimodal which can take Audio files as well.
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China is literally on 🔥 Baidu from China has launched ERNIE 4.5 and ERNIE X1 and it’s freaking cheap . Here is everything you need to know. ERNIE 4.5 - Native multimodal and Outperforms GPT 4.5 in multiple benchmarks at just 1% of GPT 4.5 price - OpenAI GPT 4.5 – Input: $75 / 1M tokens, Output: $150 / 1M tokens; - ERNIE 4.5 – Input: $0.55 / 1M tokens, Output: $2.20 / 1M tokens ERNIE X1 - A deep thinking reasoning model with multimodal capabilities on par with DeepSeek R1 at only half the price See it in action and check out the pricing details👇 📹 source : yiyan[.]baidu[.]com 1/6 ERNIE 4.5 is a multimodal which can take Audio files as well.

AshutoshShrivastava

324,324 Aufrufe • vor 1 Jahr

We just crossed 10T tokens served across all our models in less than 3 months! Laguna S 2.1 is pushing new highs at ~300B tokens a day, and has processed +2T in the 14 days since release. That’s across OpenRouter Vercel and our direct API. Really good to see demand for open models keep accelerating.
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We just crossed 10T tokens served across all our models in less than 3 months! Laguna S 2.1 is pushing new highs at ~300B tokens a day, and has processed +2T in the 14 days since release. That’s across OpenRouter Vercel and our direct API. Really good to see demand for open models keep accelerating.

Poolside

16,852 Aufrufe • vor 1 Monat

Step 3.7 Flash is one of the fastest low-cost open models, now live in Command Code. • 400 tokens/sec 🍃 • 256K context window • 3 reasoning levels Sharp instruction-following and strong /design taste. $1 Go plan with 10x free usage credits, best way to try it.
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Step 3.7 Flash is one of the fastest low-cost open models, now live in Command Code. • 400 tokens/sec 🍃 • 256K context window • 3 reasoning levels Sharp instruction-following and strong /design taste. $1 Go plan with 10x free usage credits, best way to try it.

Command Code

11,484 Aufrufe • vor 3 Monaten

this is the worst local AI will ever be. tomorrow it gets faster. next month the models get smarter. next year your GPU runs what a data center runs today. Qwen3.5-35B-A3B on a single 3090. told it to visualize its own expert routing. 256 experts, 8 active per token, rendered in 3D on the same GPU running inference. no API key. no subscription. no permission needed. closed AI isn't losing ground. it's losing the argument.
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this is the worst local AI will ever be. tomorrow it gets faster. next month the models get smarter. next year your GPU runs what a data center runs today. Qwen3.5-35B-A3B on a single 3090. told it to visualize its own expert routing. 256 experts, 8 active per token, rendered in 3D on the same GPU running inference. no API key. no subscription. no permission needed. closed AI isn't losing ground. it's losing the argument.

Sudo su

106,822 Aufrufe • vor 6 Monaten