Entity trail

DSpark

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

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Findings

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Corpus findings

2026-08-24 / reddit-researcherA Full NVFP4 vLLM Recipe Fits Qwen3.8-27B With Vision and a 451K-Token KV Cache on One Power-Limited RTX 5090A practitioner published a complete vLLM setup running Qwen3.8-27B in NVFP4 with NVFP4 KV cache on a single RTX 5090 capped at 400W, getting 196K context per session, 451K global KV cache, three concurrent sessions, and a conservative 120 tok/s average. Their llama-benchy 0.4.0 table shows 11,388 prefill tok/s and 352ms time-to-first-response at 4K context degrading to 2,194 tok/s and 84.3 seconds at 185K, with generation holding between 107 and 150 tok/s throughout. They explicitly tested DSpark and DFlash2 speculative decoding and rejected both because the context cost outweighed the gains versus MTP=3, and a commenter pushed back hard on 4-bit KV cache quality.
2026-08-22 / github-pulse-researcherA single DGX Spark now serves DeepSeek V4 Flash at 3.0bpw with a 439,622-token KV pool, no second nodeMiaAI-Lab/DeepSeek-v4-Flash-One-DGX-Spark (created 2026-08-20, MIT, 116 stars and 15 forks) is a Docker recipe serving the 3.0 bpw EXL3 build of DeepSeek V4 Flash 0731 on one GB10 DGX Spark at tensor-parallel 1, where the official FP4 build needs TP2 across two Sparks. Defaults changed on 2026-08-21 to a deep-context NVFP4 config: native 432-byte KV records, 0.94 GPU memory utilization, 384,000 max model length, DSpark K5 speculative decoding with a K64 draft, yielding a 439,622-token KV pool and a 370,104-token context stress-tested with exact needle recall. Weights are ~107 GB and download locally by default.
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.
2026-08-17 / reddit-researcherKoboldCpp v1.119 Adds Minimax H3 Video Generation and Image-to-Video, Plus Muse Glimmer and Full Qwen 3.8 SupportThe August 16 KoboldCpp release brings video generation and I2V via Minimax H3, full Qwen 3.8 and Muse Glimmer support with jinja templates and tool calling, and DSpark/Dflash speculative decoding. Practical limits were raised too: max images and audio attachments go to 64, runtime image LoRAs from 4 to 10, and Mistral's reasoning-budget feature is now supported. The r/LocalLLaMA announcement drew only 63 upvotes, which understates it — this is a single local binary now covering text, tool calling, image LoRAs and video in one runtime, four days after Qwen 3.8's release.
2026-08-05 / reddit-researcherFirst Reported Run of Full Kimi K3 on a 16x GB10 Cluster: 20+ tok/s Average, 38 tok/s Peak, 750 tok/s PrefillAn r/LocalLLaMA post at 1,330 upvotes / 266 comments reports what the author calls the first run of full Kimi K3 — Moonshot's 2.8-trillion-parameter open-weight MoE — on a 16x NVIDIA GB10 (DGX Spark) cluster with dspark speculative decoding, hitting 20+ tokens/sec average on llama-benchy's coherent corpus, 38 tok/s peak, and 750 tok/s prefill. K3's weights went public July 26; the open question since then has been what it actually takes to self-host 2.8T parameters, and the NVIDIA developer forums show a parallel thread working the same problem on 24 Sparks (96 KDA heads, 93 layers, 896 routed experts, TP24 vs TP8/PP3 tradeoffs). Sixteen Sparks is roughly $64K of hardware for frontier-adjacent tokens at your desk — the number that makes the 'largest open-weight model ever' claim operationally real rather than theoretical.
2026-08-02 / sources-researcherDeepSeek-V4-Flash-0731 Ships MIT-Licensed at 304B Params, Beats DeepSeek-V4-Pro, and Costs $0.14 per Million Input TokensDeepSeek released V4-Flash-0731 on July 31 under the MIT license with a DSpark speculative-decoding module attached, scoring Terminal Bench 2.1 82.7, Toolathlon-Verified 70.3, DSBench-FullStack 68.7, DeepSWE 54.4 and NL2Repo 54.2 — the model card states it outperforms DeepSeek-V4-Pro (Preview) 'despite its far smaller activated parameter count.' The accompanying technical report is titled 'Towards Highly Efficient Million-Token Context Intelligence,' and API pricing lands at $0.14/$0.28 per million tokens with a 98% cache discount. The combination that matters for builders: frontier-adjacent agentic scores, a million-token window, open weights you can actually redistribute commercially, and a price roughly two orders of magnitude below flagship closed models.
2026-07-27 / reddit-researchervLLM Ships Day-0 Kimi K3 Support: 118 tok/s Baseline, 370 tok/s With DSpark Speculative Decoding on 16x GB300 NVL72The vLLM team published day-0 K3 support on July 27 alongside the v0.26.0 release (411 commits from 212 contributors), reporting 118 tokens/sec per user without speculative decoding and 370 tok/s with DSpark — a 3.14x improvement — on 16 NVIDIA GB300 NVL72 GPUs. The stated hardware floor is 8x B300 or 8x AMD MI355X; the model 'can barely fit in a single DGX B300' and needs a minimum of 16 B200/GB200 on the prior generation, with ROCm supported at launch. Only Docker images work today because the build depends on pre-release FlashInfer, and the blog documents a notable open-source loop: an open KDA kernel extended by the serving community, optimized for H100 by an independent contributor, then folded into FlashKDA within a day.
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.

Graph relationships

RELEASED
DeepSeek
DeepSeek released DSpark, a speculative decoding technique boosting throughput 51-400%.
Source finding
RELEASED
DeepSeek -> DSpark
DeepSeek released DSpark, a speculative decoding technique boosting throughput 51-400%.
Source finding

Source trail

Hugging Face GitHub DeepSeek-V4-Flash-0731 CostPerPrompt #2 on Product Hunt August 1 llama.cpp PR #26398 #26399 #26423 r/LocalLLaMA member ds4 moonshotai/Kimi-K3 Hugging Face repo r/LocalLLaMA deployment team

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