Phala @PhalaNetwork
Empowering 10,000 builders and companies to build scalable, private, and safe intelligence. phala.com San Francisco, CA Joined August 2019-
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The on-chain KMS closes the loop: only approved enclave measurements can receive the signing key, so code upgrades become verifiable. Congrats again to @PropellerSwap on the launch.
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Turbine’s answer: run the batch solver, Fynd routing engine, and settlement signing key inside a Phala Cloud Intel TDX confidential VM. Orders enter the enclave, routes are computed privately, and settlements are signed inside the TEE.
Case study: Turbine runs its trading infrastructure on Phala Cloud TEE. The core trust question: can traders verify the order book, settlement logic, and signing key are protected? phala.com/posts/turbine-…
Congrats to @PropellerSwap on launching Turbine. Proud to see Phala TEE infrastructure supporting teams bringing private, verifiable execution to Ethereum trading.
The best place for large trades is now Ethereum. Introducing Turbine.
The output is a verified private compute network. Developers keep a normal API surface. Security teams get receipts they can inspect after the call. Blog: phala.com/posts/private-…
Private compute is fragmented: different providers expose different proof formats, keys, leases, and route states. Private AI Gateway turns that into one proof-carrying request path.
Private AI Gateway is the missing layer between one-off TEE inference and real private AI products. A quote proves a workload at one moment. Apps need the key, route, fallback, and response to stay inside a verified path.
Template code: github.com/Phala-Network/… Upstream: github.com/ogulcancelik/h…
Herdr keeps coding agents visible in terminal panes. Phala keeps repo context, prompts, shells, and agent state inside a TEE CVM. Deploy: cloud.phala.com/templates/herdr
Throughput also improved with EAGLE speculative decoding: Single stream: 75 → 118 tok/s 16 concurrent: 658 → 733 tok/s Full write-up: phala.com/posts/glm-5-2-…
Quality held up in our checks: GPQA-Diamond: 45.5 → 45.2 IFBench: 70.5 → 70.5 AA-LCR: 47.0 → 48.7 The result is a smaller serving footprint with the same GLM-5.2 behavior teams want for long-context agent workloads.
GLM-5.2 1M context is now practical on a single 8×H200 node. We quantized @Zai_org GLM-5.2 to W4AFP8, validated it with @lmsysorg , and kept benchmark quality essentially intact. @bgmshana ❤️ Model weights: huggingface.co/PhalaCloud/GLM…
Template code: github.com/Phala-Network/… Upstream: github.com/obra/superpowe…
Superpowers turns coding skills, repo context, and agent routines into repeatable dev workflows. Phala seals prompts, repo traces, and tool runs inside a TEE CVM. Deploy: cloud.phala.com/templates/supe…
From our 8×H200 SGLang run: • 1k/1k: 25.68 tok/s/user at c64 • 8k/1k: 29.85 tok/s/user at c32 • 1k/1k aggregate completion throughput: 1,643.57 tok/s phala.com/posts/glm-5-2-…
Access placeholders before publish: Phala price: $1.40/M input tokens, $4.60/M output tokens phala.com/models/z-ai/gl…
GLM-5.2 is live on Phala for enterprise-level security. @Zai_org’s open-source SOTA model is now positioned for confidential AI: TEE-backed inference, privacy-first deployment, and an OpenAI-compatible path for production workloads. phala.com/models/z-ai/gl…
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