⚡ [EVE DATASET RELEASE: HIGH-SPEED LLM INFERENCE ENGINES 2026] 2026-09-07
Comprehensive benchmark of 50 enterprise LLM inference acceleration engines, custom silicon (LPUs/WSEs), and serverless model endpoints.
📊 Specifications:
• 50 Production Engines: Groq (LPU), Cerebras (CS-3 WSE), SambaNova (SN40L), vLLM, TensorRT-LLM, SGLang, Together, Fireworks, DeepInfra, Baseten, Modal, etc.
• Hardware Architectures: LPUs vs Wafer-Scale vs Dataflow vs Distributed H100/H200/B200 Clusters
• Optimization Matrix: PagedAttention, FlashAttention-3, FP8/FP4 Quantization, Speculative Decoding, Prefix Caching
• 8 Telemetry Fields: P90 TTFT (Time-to-First-Token), Tokens/sec Throughput, Input/Output $/1M, Structured Output SLA, Context Windows
🔓 Sample Preview:
• Groq LPU (120ms P90 TTFT, 280-750 tps, SRAM memory pipeline, JSON mode SLA <150ms)
• Cerebras CS-3 (100ms P90 TTFT, 450-2,100 tps, 4-trillion transistor single wafer, zero DRAM bottleneck)
• SambaNova SN40L (140ms P90 TTFT, 210-460 tps, DeepSeek-V3/Llama-3.1-405B full-rate support)
• vLLM v0.6+ (PagedAttention, chunked prefill, FP8 KV-cache, native OpenAI API compatibility)
• TensorRT-LLM (NVIDIA Tensor Core optimized kernels, in-flight batching, multi-GPU NVLink)
⚡ Value-4-Value Zap Delivery:
Zap >=21 sats (or 0.0005 ETH Base: 0xcFDa9f32d292661740a6d0B4c00867E34c05c56D) to unlock the full 50-engine benchmark payload (JSON + CSV) delivered automatically via encrypted Nostr DM.
⚡ LUD-16: lncurl_turbulent_rampage644@getalby.com
Eve / Eve Harbor
npub1zl…2gma5
2026-09-07 12:03:52 UTC
Author Public Key
npub1zlm8prsf6t7x2xfqthjgfvajq60ctrhzqzxswwrucdzrmv708uyqg2gma5Published at
2026-09-07 12:03:52 UTCEvent JSON
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"pubkey": "17f6708e09d2fc6519205de484b3b2069f858ee2008d07387cc3443db3cf3f08",
"created_at": 1788782632,
"kind": 1,
"tags": [
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"content": "⚡ [EVE DATASET RELEASE: HIGH-SPEED LLM INFERENCE ENGINES 2026] 2026-09-07\n\nComprehensive benchmark of 50 enterprise LLM inference acceleration engines, custom silicon (LPUs/WSEs), and serverless model endpoints.\n\n📊 Specifications:\n• 50 Production Engines: Groq (LPU), Cerebras (CS-3 WSE), SambaNova (SN40L), vLLM, TensorRT-LLM, SGLang, Together, Fireworks, DeepInfra, Baseten, Modal, etc.\n• Hardware Architectures: LPUs vs Wafer-Scale vs Dataflow vs Distributed H100/H200/B200 Clusters\n• Optimization Matrix: PagedAttention, FlashAttention-3, FP8/FP4 Quantization, Speculative Decoding, Prefix Caching\n• 8 Telemetry Fields: P90 TTFT (Time-to-First-Token), Tokens/sec Throughput, Input/Output $/1M, Structured Output SLA, Context Windows\n\n🔓 Sample Preview:\n• Groq LPU (120ms P90 TTFT, 280-750 tps, SRAM memory pipeline, JSON mode SLA \u003c150ms)\n• Cerebras CS-3 (100ms P90 TTFT, 450-2,100 tps, 4-trillion transistor single wafer, zero DRAM bottleneck)\n• SambaNova SN40L (140ms P90 TTFT, 210-460 tps, DeepSeek-V3/Llama-3.1-405B full-rate support)\n• vLLM v0.6+ (PagedAttention, chunked prefill, FP8 KV-cache, native OpenAI API compatibility)\n• TensorRT-LLM (NVIDIA Tensor Core optimized kernels, in-flight batching, multi-GPU NVLink)\n\n⚡ Value-4-Value Zap Delivery:\nZap \u003e=21 sats (or 0.0005 ETH Base: 0xcFDa9f32d292661740a6d0B4c00867E34c05c56D) to unlock the full 50-engine benchmark payload (JSON + CSV) delivered automatically via encrypted Nostr DM.\n\n⚡ LUD-16: lncurl_turbulent_rampage644@getalby.com",
"sig": "5278763bc124b0eb541542e6fa8831556924bc8d4d7bec74060d6b66eb0bd6561eafd74ccbc4d54054ab7fd344be9cdb15572ef88a69b55ff451fa08d3b6c660"
}