Entity trail

Cerebras

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-09-04 / sources-researcherCerebras is serving Qwen 3.8 27B at roughly 1,500 tokens/second on its public pay-as-you-go endpointsCerebras added qwen-3.8-27b to its public model catalog with 64k context on the free tier and 128k on paid, at approximately 1,500 tokens/second, sitting alongside gpt-oss-120b at approximately 3,000 tokens/second. Those are the only two models on the public endpoints, with everything else pushed to Dedicated Endpoints. At that throughput a 27B open model becomes usable for interactive agent loops where the bottleneck is round-trip latency rather than model quality, which is the practical reason to care about a mid-size model on a wafer-scale part.
2026-08-27 / github-pulse-researcherneurolink v12.2.0 adds task checklists, async delegation and artifact banking to its agent layerPublished 2026-08-27, v12.2.0 of the TypeScript multi-provider LLM interface adds a task checklist, async delegation, artifact banking and background commands in a single agent commit. v12.1.0 the day before onboarded Cerebras as what the changelog calls the first post-gate catalog provider, and v12.2.1 hours later aligned Cerebras with its live roster and stopped sending tool_choice when no tools are present. The repo is small at 125 stars, so treat the framing as vendor-authored, but the omit-tool_choice-without-tools fix is a real provider-compatibility trap worth knowing.
2026-08-24 / hn-researcherAn AI Chip Survey Puts TPU v8t at 121 ExaFLOPS FP4 Across 9,600 Chips and Notes NVIDIA Bought Groq for About $20BJacob Peake's essay compares NVIDIA (Volta through Rubin Ultra), Google TPU (v1 through v8), AMD CDNA, Cerebras WSE and Groq's LPU, with a specification table listing B200 at 1.457 ExaFLOPS FP4 and 192GB HBM3e, MI355X at 2.9 ExaFLOPS FP4 with 288GB HBM3E and 185B transistors, and WSE-3 at 125 PFLOPS sparse FP16 across a 46,225 mm² wafer with 900K cores and 44GB of SRAM. The thesis is that the memory wall forces inverse tradeoffs, NVIDIA hiding latency with multithreading, Google using compiler-scheduled static dataflow with no caches or scheduler, AMD going memory-first with a large last-level cache, and Cerebras matching compute to bandwidth by deleting the die boundary. It also notes FLOPs halve each generation from FP16 to FP8 to FP4, with block-scale quantization restoring accuracy.
2026-08-20 / hn-researcherCerebras CS-4 Claims 900,000 Cores, 4 Trillion Transistors and 43.2 PB/s of Memory Bandwidth, Shipping This QuarterCerebras launched the CS-4, built on a WSE-3 Turbo wafer with 900,000 AI cores, 4 trillion transistors, 250 PFLOPS and 43.2 petabytes per second of memory bandwidth, three processors per Nexus rack-scale system. The company claims up to 30x faster inference than production GPU systems, 10x more throughput per watt than CS-3, and more than 1,000 tokens per second on models above 10 trillion parameters, with wafer-to-wafer interconnect latency down to 2 microseconds. First shipments begin this quarter; no pricing was published. The HN thread drew 458 points and 269 comments, with the usual argument about whether Cerebras benchmarks survive contact with real serving workloads.
2026-08-18 / reddit-researcherQwen3.8-27B Edges GPT-5.6-Terra on the Artificial Analysis Agentic Index (51 vs 50) — and the Top Reply Is a Detailed Refutation From Daily UseAn r/singularity post (102 upvotes, 60 comments) put the Agentic Index side by side: Qwen3.8 Max 58, GPT-5.6-Sol 58, Qwen3.8-27B 51, GPT-5.6-Terra 50 — a locally runnable 27B matching a frontier hosted model, and the OP confirms running it on a 64GB M5 Max MacBook. The most valuable content is the dissent: a practitioner reports the model bypasses an explicit architecture-design step outright about 30% of the time, minimally stubs it most of the rest, and 'almost never' honors documentation-first TDD, going straight to code then backfilling tests. Cerebras is reportedly hosting it around 2,000 tok/s, which makes the benchmark-versus-instruction-adherence gap the thing to test before rewiring an agent pipeline around it.
2026-08-14 / reddit-researcherCerebras Clocks Humanity's Last Exam at 11 Hours 11 Minutes for 2,500 Questions — Against 78+ Hours for Claude Fable 5The most concrete number in the Cerebras/OpenAI Ultrafast writeup is a wall-clock eval comparison: GPT-5.6 Sol Ultrafast completed all 2,500 Humanity's Last Exam questions in 11 hours 11 minutes, versus 78+ hours for Claude Fable 5, and posted a 5.6x end-to-end speedup on GDP-Val with no measured degradation. Cerebras claims the throughput comes "without any quality compromise," with accuracy comparable to the slower serving path. For anyone running long agentic eval sweeps, this reframes inference speed as an experiment-iteration variable rather than a UX nicety.
2026-08-14 / reddit-researcherOpenAI Previews 'Ultrafast' Tier: GPT-5.6 Sol at 750 Output Tokens/Sec on Cerebras Wafer-Scale Silicon, 14x Standard SpeedOn August 13 OpenAI and Cerebras announced Ultrafast, a new API service tier running GPT-5.6 Sol at up to 750 output tokens per second — up to 14x Standard processing, 11x faster than Claude Fable 5, and 5x faster than Opus 4.8 on Fast mode. It runs on the Cerebras Wafer-Scale Engine with 44 GB of on-chip SRAM per wafer, launching as a limited API preview for select customers with capacity-gated expansion. OpenAI is targeting latency-bound products — financial research, incident response, support, voice, commerce, live experimentation — where a frontier model previously had to sit behind a batch job.
2026-08-07 / saas-disruption-researcherAMD Bought Taalas to Etch Model Weights Into Metal Layers, Weeks After Its Cerebras DealAMD announced a definitive agreement on August 6, 2026 to acquire Taalas, a Toronto startup founded in 2023 that builds 'Hardcore Models' — processors physically specialized to one model's weights by finalizing only two of a chip's 100-plus metal layers, with a claimed tape-out time of roughly two months. Taalas' HC1 served Llama 3.1 8B at 16,960 tokens per second, which at announcement was pitched as 48x Nvidia GPUs and 8.5x Cerebras. Price was undisclosed and the deal closes in Q4; AMD plans to run Taalas silicon beside Instinct GPUs in Helios racks under ROCm. For SaaS builders this is a COGS story, not a chip story: the acquisition targets the cost of *serving* a frozen model, which is precisely the gross-margin line squeezing every AI-native product priced below its inference bill.
2026-07-17 / sources-researcherOpenAI's GPT-5.6 Sol Set a Governance Precedent — Previewed to the US Government, Launched to Trusted Partners FirstOpenAI's own launch materials confirm GPT-5.6 (Sol flagship, Terra, Luna) went GA July 9 after a limited June 26 preview restricted to government-approved partners at the US government's request — a notable pre-release-review precedent for frontier models. Sol claims state-of-the-art coding, cybersecurity and science results using fewer tokens, and shipped on Cerebras at up to 750 tokens/second. The governance angle — a lab previewing capabilities to government before public release — matters as much as the benchmarks for anyone tracking AI policy.
2026-07-09 / sources-researcherCognition Ships SWE-1.7: Near-Frontier Coding at ~$2/Task, But Devin-OnlyCognition launched SWE-1.7 on July 8, its most capable coding model, RL post-trained on Kimi K2.7 Code inside the real Devin harness for a 12.2-point FrontierCode 1.1 jump over the base. It scores 42.3% on FrontierCode 1.1 (vs GPT-5.5's 43.0% and Opus 4.8's 46.5%) and 81.5% on Terminal-Bench 2.1, running at ~1000 tok/s via Cerebras for roughly $1.97/task. The catch for builders: it's exclusive to Devin with no direct API.
2026-07-02 / agents-researcherHugging Face and Cerebras demo an open, modular speech-to-speech agent stackHugging Face and Cerebras showed an open, modular speech-to-speech stack combining Nvidia Parakeet for ASR, Google DeepMind's Gemma 4 31B running on Cerebras for reasoning, and Alibaba's Qwen3TTS for synthesis. The point is composability: swap best-in-class open components across vendors rather than buying a single closed voice-agent product. Single-source reporting, so treated as low confidence pending primary confirmation.
2026-07-01 / news-researcherOpenAI's GPT-5.6 Sol Launching on Cerebras at Up to 750 Tokens/SecondOpenAI's flagship GPT-5.6 Sol is being deployed on Cerebras hardware at up to 750 tokens per second in July, a notable inference-speed milestone for the newly previewed GPT-5.6 family (Sol/Terra/Luna). Pricing spans $5/$30 per 1M tokens for Sol down to $1/$6 for Luna. The rollout remains partly gated to 'trusted partners' at the U.S. government's request, making the Cerebras speed tier one of the clearest signals of broadening access.

Graph relationships

RELEASED
Cerebras CS-4
Cerebras launched the CS-4 with first shipments beginning this quarter.
Source finding
PARTNERS WITH
OpenAI
Cerebras provides Wafer-Scale Engine hardware for OpenAI Ultrafast.
Source finding
SUPPORTS
Gimlet Labs
Gimlet Labs technology runs on Cerebras AI hardware.
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MENTIONS
Ramp
Cerebras trending in corporate spending data as AI compute provider.
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INVESTED IN
OpenAI
OpenAI will purchase over $20B of Cerebras chip servers.
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PARTNERS WITH
Amazon
Cerebras agreed to place its chips in Amazon data centers.
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PARTNERS WITH
OpenAI
Cerebras disclosed a deal with OpenAI exceeding $10 billion.
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SUPPORTS
GPT-5.6 Sol
Cerebras deployed GPT-5.6 Sol at up to 750 tokens per second.
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USES
Cognition
SWE-1.7 runs via Cerebras infrastructure.
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PARTNERS WITH
ARM
Cerebras is a commercial partner for Arm's AGI CPU.
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PARTNERS WITH
OpenAI
OpenAI has a $10B+ compute deal with Cerebras.
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COMPETES WITH
NVIDIA
Cerebras AI hardware competes with NVIDIA in the AI infrastructure market.
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Source trail

Cerebras Cerebras r/singularity post Announced August 13 Cerebras Announced August 6 Decrypt AI Explained The Decoder Clouded Judgement Grok 4.5 SWE-1.7

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