Entity trail

SGLang

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-09 / arxiv-researcherMiles v0.1 Runs Fully Asynchronous Agentic RL on a 744B GLM-5.2 at 263 Seconds Per StepMiles v0.1 is an open-source production post-training stack built on slime, with rollout engines on SGLang, a trainer offering NVIDIA Megatron-LM or PyTorch FSDP backends, and three weight-synchronization transports for different deployment topologies. Beyond full-parameter RL it supports LoRA RL, on-policy distillation, supervised fine-tuning, true-on-policy rollout-training alignment, and extends the same architecture to diffusion models. The end-to-end case study runs fully asynchronous agentic RL on a GLM-5.2 744B-A40B model over terminal-use coding tasks across 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps.
2026-09-08 / hn-researcherOpenBMB Releases MiniCPM5-2B Under Apache 2.0 With a 131K Context and the Top Intelligence Index Score for Any Open Model Under 4BOpenBMB published MiniCPM5-2B on September 7, a 2B-parameter dense reasoning model with 42 layers, grouped-query attention, BF16 weights, a 131,072-token context and a LlamaForCausalLM architecture, licensed Apache 2.0 on Hugging Face. It scores 15 on Artificial Analysis Intelligence Index v4.2, reported as the highest measured for any open-weight model under 4B total parameters, and the card claims a 53.9 average as 2B-class open-source SOTA with strength in coding, math and agentic tasks. Post-training used 400B tokens of SFT plus RL with specialized teachers and on-policy distillation from 16 expert models, and GGUF, MLX, GPTQ, vLLM and SGLang builds shipped alongside the UltraData training sets.
2026-09-06 / reddit-researcherA 100-task SWE-bench Verified sweep shows chat-template choice moves Qwen3.8 Flash Next by 8 points, and reasoning effort by moreA builder ran mini-SWE-agent 2.4.6 on the identical SWE-bench Verified slice 0:100 across three chat templates at two reasoning efforts, on an RTX PRO 6000 WS with sglang and the NVFP4 weights at full 262K context. Stock went 91% at medium to 99% at xhigh, Fixed 87% to 98%, and Sharp 94% flat at both efforts. The tradeoff is time and tokens: stock xhigh cost +143.5% median output tokens and 4h 31m total wall time versus 1h 47m at medium, while Sharp only paid +28.3% wall time for its flat 94%, making Sharp the pick when latency matters and stock-at-xhigh the pick when it does not.
2026-09-05 / github-pulse-researcherSGLang 0.5.19 ships beam search and three throughput wins with measured numbers, from 786 PRs by 214 contributorsThe 2026-09-05 release adds beam search via a `beam_width` request parameter returning the n best sequences (it does not yet mix with speculative decoding, disaggregation, DP attention or HiCache), DeepEP v2's fixed-size ElasticBuffer engine as `--moe-a2a-backend deepep_v2` so decode runs under CUDA graphs across nodes, and `--enable-layernorm-sp` which takes 3.5% off Qwen3-8B prefill on H100 and 5.6% on B200. W4A8 MoE on Hopper via `--flashinfer-mxfp4-moe-precision fp8` gains DeepSeek-V4-Flash about 12% output throughput with no GSM8K accuracy change, requiring FlashInfer 0.6.18.
2026-09-05 / projects-researchersgl-project/sglang carries 4,292 open PRs against 857 open issues, a 5:1 inversion of the usual serving-framework backlogThe LLM serving framework gained 836 stars today to reach 35,498 with 8,573 forks, and its 5,149 open_issues_count splits almost entirely to pull requests. Every other high-traffic repo checked today runs the opposite way, oh-my-pi at 910 PRs to 1,393 issues and cockpit-tools at 55 to 532. A backlog dominated by unmerged PRs at this scale means contribution throughput, not user complaints, is the bottleneck.
2026-08-29 / reddit-researcherQwen3.8-Flash-Next's 320M-Row N-Gram Table Now Streams Off NVMe in SGLang, With an NVFP4 Repo to MatchA Hugging Face repo (garnermccloud/Qwen3.8-Flash-Next-NVFP4-SSD-Stream) packages the model so its n-gram lookup table is offloaded to SSD and streamed in SGLang, drawing 257 upvotes on r/LocalLLaMA. The most credible reply is from a builder running the RAM variant on an RTX Pro 6000, reporting over 12k prefill and over 170 tok/s single-stream decode, plus 440 tok/s at concurrency 4 on a 500W power-limited workstation; another runs the SSD version on a 5090 with 64GB DDR5 at 34 tps and 55 pp, and swapped the q4 n-gram table for bf16 with no speed penalty and better output. The reason it works, per the thread, is that n-gram lookups are predictable enough to prefetch, so SSD latency stops mattering.
2026-08-27 / reddit-researcherLMSYS Published the Real Qwen3.8-Flash-Next Deployment Numbers: Offloading the 51B N-Gram Table Frees 23.46 GiB Per GPU and Grows KV Cache 78.5%LMSYS's day-0 SGLang post dated 2026-08-26 gives the numbers the release-day megathread did not: 125B main parameters plus a separate 51.2B Per-Layer Embedding table (about 95.4 GiB in BF16), 6B activated per token, 48 layers split 36 GDN linear attention and 12 QSA sparse attention, 512 experts with top-10 routing. Sparse pinned-host offload of the PLE table on H200 with TP4 drops weight footprint from 83.91 to 60.45 GiB per GPU and raises KV cache capacity from 1.84M to 3.28M tokens. On B200 TP4 with MTP speculative decoding it hits 540 tok/s at batch size 1 with an accept length of 3.3.
2026-08-25 / thought-leaders-researcherBoyd Kane: a malicious open-weight LLM could take over its host by emitting tokens that exploit the inference engineIn an essay published August 25, Boyd Kane argues the token stream itself is an unguarded attack surface: an LLM can emit a sequence whose semantics are irrelevant but which triggers a parser bug in vLLM or SGLang, giving it code execution on a box that holds its own weights and has datacentre network access. He anchors it in CVE-2025-9141, where vLLM's XML tool parser for Qwen3 Coder passed nearly every tool-call argument to eval(), and notes the lead maintainer force-merged the PR despite Gemini's automated review flagging it critical. His proposed defense is architectural, split GPU execution from token parsing onto different machines and strip the host's permissions, which is directly actionable for anyone self-hosting open weights.
2026-08-24 / hn-researcherQwen 3.8 27B Broke a Commercial License Check in 30 Minutes on a 128GB Local Box, Recovering an RSA Key by Static Analysis AloneReported 2026-08-22, a Lenovo ThinkStation PGX (Nvidia GB10 Grace Blackwell, 128GB unified memory, 273 GB/s) running Qwen 3.8 27B under SGLang with NVFP4 and DFlash2 mapped a commercial app's licensing architecture, including signature verification, machine binding, revocation lists and signed update paths, then produced a bypass. It never executed the binary, working only through standard Bash tooling, and self-corrected when its first key extraction failed an integrity check. The 273 GB/s bandwidth figure matters more than the parameter count here, since this was long-horizon agentic work previously assumed to need a frontier endpoint.
2026-08-22 / arxiv-researcherFlashPrefill V2 Reports 47x Prefill Speedup Over FlashAttention-2 at 128K Context and Ships as an SGLang BackendFlashPrefill V2 turns the earlier block-sparse prefill prototype into something deployable: a mean correction term suppresses approximation error at extreme sparsity, the operator is rebuilt with PackGQA memory access, warp specialization and pingpong pipelining to align with FlashAttention-3/4, and FP8 inference is supported. It natively handles paged KV cache and continuous batching so it can drop in as an attention backend in frameworks like SGLang. On NVIDIA H20 GPUs it reports up to 47.26x and 27.19x speedups over FlashAttention-2 at 128K context in FP8 and BF16, and still 30.49x over an FA3/4-aligned dense baseline in FP8.
2026-08-21 / rss-researcherLiquid AI's DSpark draft models hit 3.18x throughput on GPU and cut function-calling latency 57%LFM2.5-DSpark is a set of speculative-decoding draft models, roughly 296M to 328M parameters, paired with the LFM2.5 1.2B, 2.6B and 8B-A1B targets. Liquid reports up to 3.18x throughput on GPU and 2.87x on-device, with the 2.6B hitting 2.67x on an H100 and 2.27x on an M4 Max MacBook, and function-calling latency down 57% on average. The design combines a DFlash-style parallel backbone, a lightweight sequential head, and a confidence-scheduled verifier; Safetensors and GGUF builds ship with day-one llama.cpp and SGLang support.
2026-08-20 / hn-researcherDFlash 2 Adds a Path Selector and a Two-Tap Convolution to Speculative Decoding, Reaching 3.4x Throughput on Qwen3.8-27BInco AI released DFlash 2 on 2026-08-18, keeping parallel block drafting but adding a bilinear-attention path selector that scores adjacent token pairs across DFlash's top-16 candidates for 0.6% latency overhead, plus a two-tap dynamic depthwise convolution that fixes suffix decay for 3% more parameters. The result is a 21% gain in mean acceptance length over DFlash, 2.7-3.4x throughput versus autoregressive decoding on Qwen3.8-27B, and 3.1-4.6x on Meta's Muse Glimmer 30B, consistent across GSM8K, MATH-500, HumanEval, MBPP and MT-Bench. Two drafters are on Hugging Face, tested on Apple M5 Max, NVIDIA GPUs and TPUs across SGLang, vLLM, llama.cpp, Ollama and oMLX.

Graph relationships

USES
Miles
Miles has rollout engines on SGLang
Source finding
USES
Mini-SWE-agent
mini-SWE-agent runs on RTX PRO 6000 WS with sglang serving engine
Source finding
USES
FlashInfer
SGLang 0.5.19 requires FlashInfer 0.6.18 for W4A8 MoE quantization support.
Source finding
USES
Qwen3.8-Flash-Next
Qwen3.8-Flash-Next runs in SGLang with SSD-streamed n-gram table optimization.
Source finding
USES
FlashPrefill V2
FlashPrefill V2 ships as an SGLang backend for attention computation.
Source finding
SUPPORTS
Qwen3.8-2.4T-A95B
SGLang shipped day-0 support for Qwen3.8-2.4T-A95B.
Source finding
USES
Miles
Miles composes SGLang for rollout.
Source finding
USES
XGrammar
XGrammar is default structured output backend in SGLang
Source finding
COMPETES WITH
vLLM
SGLang serves as a drop-in replacement for vLLM
Source finding
SUPPORTS
kvcached
kvcached integrates with SGLang for KV cache management.
Source finding
SUPPORTS
kvcached
kvcached supports SGLang serving engine
Source finding
SUPPORTS
kvcached
kvcached enables RadixCache support for SGLang.
Source finding

Source trail

arXiv 2609.08368 MiniCPM5-2B GitHub arXiv 2609.03966 ARC Prize r/LocalLLaMA r/LocalLLaMA LMSYS Hugging Face Muse Glimmer unsloth/Muse-Glimmer-30B-GGUF Meta's technical blog

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