Entity trail

EAGLE-3

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-07-19 / skill-finderEAGLE-3 speculative decoding on vLLM is delivering ~2x throughput on AMD InstinctAs of July 13, 2026, AMD Quark is training, quantizing, and serving EAGLE-3 speculative-decoding drafts through vLLM on AMD Instinct GPUs, reporting up to 2.00x throughput for Kimi-K2.5 and 1.79x for MiniMax-M2.5. Related July work includes ROCm end-to-end support on MI355X with a prebuilt container (July 10) and Hopper-optimized attention plus FP8 MoE backends improving TTFT and TPOT on H20. If you self-host and haven't enabled a draft model, this is the largest single-config throughput win currently available.
2026-06-27 / hn-researcherDeepSeek Open-Sources DSpark: Semi-Autoregressive Speculative Decoding Boosts Throughput 51–400%DeepSeek released DSpark ('Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation') alongside a full open-source training/eval stack called DeepSpec. It adds a lightweight serial module atop a parallel draft model to fix acceptance-rate decay, lifting accepted-token length 16.3–30.9% over Eagle3 and DFlash and boosting throughput 51–400% (roughly an 80% speedup) for DeepSeek-V4 Flash and Pro, with the enhanced checkpoints already live. The paper PDF was the #1 story on Hacker News at 510 points.
2026-06-24 / skill-finderEnable speculative decoding only at low batch sizes — it's net-negative when the GPU is already saturatedSpeculative decoding (a small draft model proposes tokens the target verifies in parallel) delivers 2–5x latency wins, but only in memory-bound, low-batch regimes; at large batch sizes the GPU is already compute-bound and the extra draft and verification work makes inference slower than plain autoregressive decoding. The actionable rule: gate speculative decoding on batch size rather than leaving it always-on. SGLang's EAGLE implementation handles this adaptively — it tracks an EMA of accepted draft length and switches between predefined speculative-length tiers (e.g., [1,3,7]), each with a pre-captured CUDA graph so tier changes are cheap.
2026-05-29 / github-pulse-researcherNVlabs Eagle Trends at 1.4K Stars With +264 Today: NVIDIA's Frontier Vision-Language Model Ships 512-Frame Video Comprehension and Eagle-Video-110K DatasetNVlabs/Eagle is NVIDIA's family of frontier vision-language models designed for long-context multimodal learning. Eagle 2.5 supports up to 512 video frames for long video comprehension (few minutes to 3 hours), trained jointly on image and video data. Ships with Eagle-Video-110K, a novel dataset with story-level and clip-level annotations. Eagle2-9B achieves state-of-the-art across multimodal benchmarks, matching models with up to 70B parameters at a fraction of the size.
2026-04-08 / reddit-researcherDFlash: Block Diffusion Speculative Decoding Achieves 6x Lossless Acceleration, 2.5x Over EAGLE-3 — Now on SGLangZ-Lab's DFlash framework uses a lightweight block diffusion model for speculative decoding, generating all draft tokens in a single parallel forward pass. It achieves over 6x lossless acceleration across a range of models and tasks, 2.5x higher speedup than state-of-the-art EAGLE-3. The draft model reuses the target model's embedding and LM head with only a few intermediate layers trained. Works under sampling (temperature=1) and thinking mode with ~4.5x acceleration for reasoning models. Now supported on SGLang. Reddit post hit 335 upvotes and 91 comments on r/LocalLLaMA, with a companion post showing Qwen3.5 27B running at ~65 tok/s with DFlash on 2x 3090 (66 upvotes).
2026-03-16 / skill-finderMirror Speculative Decoding: Heterogeneous GPU+NPU Parallel Draft-Verify for 2.8–5.8x Inference SpeedupMirror-SD (ICLR 2026) eliminates the synchronous bottleneck in standard speculative decoding by running draft-model speculation and target-model correction paths simultaneously on heterogeneous hardware—draft to NPU, target to GPU—removing resource contention while maintaining mathematical output equivalence to the original model. Speculative streaming emits multiple draft tokens per step while the target model processes the previous batch in parallel, and cross-device parallelism on commodity GPU+NPU setups is the key deployment primitive. Benchmarks show 2.8x–5.8x wall-time speedup on SpecBench across 14B–66B parameter models—a 30% relative improvement over the strongest EAGLE3 baseline—making this the current state-of-the-art for latency-sensitive production inference without quality degradation.
2026-03-07 / saas-disruption-researcherCloudEagle Launches EagleEye: Agentic AI for Autonomous SaaS and AI Lifecycle ManagementCloudEagle launched EagleEye, an agentic AI that autonomously manages the enterprise AI/SaaS lifecycle — provisioning, policy enforcement, renewals, spend optimization, and compliance remediation. Key stat: 60% of AI and SaaS tools remain completely outside IT visibility; 48% of former employees still have active app access months after leaving. The platform offers 500+ direct integrations and 30-minute onboarding. **Builder opportunity**: Shadow AI governance is becoming a category in its own r

Graph relationships

USES
MiniMax-M2.5
MiniMax-M2.5 achieves 1.79x throughput using EAGLE-3 speculative decoding on AMD Instinct.
Source finding
USES
Kimi K2.5
Kimi-K2.5 achieves 2.00x throughput using EAGLE-3 speculative decoding on AMD Instinct.
Source finding
COMPETES WITH
DFlash
DFlash achieves 2.5x higher speedup than EAGLE-3.
Source finding
USES
MiniMax-M2.5 -> EAGLE-3
MiniMax-M2.5 achieves 1.79x throughput using EAGLE-3 speculative decoding on AMD Instinct.
Source finding
COMPETES WITH
DFlash -> EAGLE-3
DFlash achieves 2.5x higher speedup than EAGLE-3.
Source finding

Source trail

vLLM Blog DeepSeek (GitHub) / Hacker News PremAI — Speculative Decoding: 2-3x Faster LLM Inference (2026)GitHub r/LocalLLaMA / Z-Lab ICLR 2026 — Mirror Speculative Decoding IndyStar/PR

Graph sources

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