Entity trail

DGX Sparks

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-29 / reddit-researcher181 tok/s Aggregate on 2x DGX Spark, With a mmap Fix That Cut One Prefill's Disk Reads From 603 GB to 19 GBA builder posted a full recipe hitting 181 tok/s aggregate (peaking at 195) across ~9 concurrent agent sessions on two DGX Sparks running Qwen3.8-Flash-Next NVFP4 at 512K context, with single-stream decode at 30-50 tok/s. The nodes are joined by a direct ConnectX-7 cable over NCCL/RoCE at 200 Gb with TP=2, and the post warns the TCP fallback is silent and costs half the speed, so you must confirm 'Using network IB' in the NCCL log. Mapping the 47.7 GiB FP8 n-gram table off NVMe dropped per-node weights from 65 to 41 GiB, and two fixes made it fast: madvise(MADV_RANDOM) killed a 30x read amplification (one 405K prefill read 603 GB from disk before, 19 GB after) and 64 gather threads removed fault-latency serialization, freeing memory for a 2.89M-token KV pool.
2026-08-23 / reddit-researcherA Homelab Operator Is Expanding a DGX Spark Cluster From 16 to 36 Nodes for 4.6TB of Unified MemoryThe builder behind what they claim was the first 16x DGX Spark cluster is adding 20 more Sparks, wired with one 24x200Gb QSFP56 plus 8x400Gb switch, 24 DAC cables and six 400Gb-to-2x200Gb breakouts. The rack is partitioned into 'inference modules' rather than one big serving target, with 16 nodes reserved for SOTA models like Kimi K3 and the rest doing rerank, embeddings, video, image and audio, coordinated as one persistent agent via Hermes and a custom memory sidecar. The stated reason for Sparks over B200/B300 is homelab cooling and power, plus easier resale liquidity.
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.
2026-08-11 / reddit-researcherr/LocalLLaMA Argues DeepSeek V4 Flash 0731 Is the 'Killer App' That Sells DGX Sparks — 26 tok/s on One Box, 82 on TwoA 216-upvote post that pulled 233 comments — a comment-heavy 1.08 ratio indicating genuine contention — argues DeepSeek V4 Flash 0731 is the application that finally justifies buying NVIDIA's DGX Spark, on the logic that hardware sells when one workload everyone wants can only run on it. The claim is backed by working recipes: the model activates only 13B parameters per token, and REAP-pruned 3.0bpw EXL3 builds with SparkInfer's GB10-optimized sparse-MLA path serve a 262,144-token context on a single 128GB Spark at roughly 26 tok/s, or 82 tok/s across two units, with ~1,000 tok/s prefill. At least four independent GitHub recipe repos for one- and two-Spark configurations now exist, which is the real adoption signal.
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.
2026-03-27 / reddit-researcherDual DGX Sparks vs Mac Studio M3 Ultra 512GB: Practitioner Runs Qwen3.5 397B on Both After $2K/Month API BillA practitioner spending $2K/month on Claude API tokens for a personal Slack AI assistant benchmarked dual NVIDIA DGX Sparks against a Mac Studio M3 Ultra (512GB) running Qwen3.5 397B locally. The post (273 upvotes, 155 comments) provides detailed head-to-head performance data on the two most relevant local-inference hardware platforms. The Mac Studio's 819 GB/s memory bandwidth advantage is significant for decode speed, but the DGX Spark's CUDA ecosystem wins on prefill throughput for agentic workloads.
2026-03-17 / rss-researcherNVIDIA GTC Frames RTX PCs and DGX Sparks as Personal AI Devices Running NemoClaw LocallyNVIDIA's GTC 2026 blog post positions consumer RTX PCs and DGX Sparks as a new 'personal AI device' category — explicitly parallel to smartphones — running NemoClaw and open models locally without cloud dependency. The post covers local agent workflows that persist on personal hardware at all times. This is the first time NVIDIA has framed consumer compute as an agent runtime rather than just a rendering or inference accelerator.

Graph relationships

COMPETES WITH
Mac Studio M3 Ultra
DGX Sparks and Mac Studio M3 Ultra are compared for running Qwen3.5 397B locally.
Source finding

Source trail

r/LocalLLaMA r/LocalLLaMA (460 upvotes, 347 comments)GitHub r/LocalLLaMA (216up/233c), corroborated by GitHub recipe repos and Local Inference Lab SparkInfer docs r/LocalLLaMA (1,330 upvotes / 266 comments)r/LocalLLaMA NVIDIA AI Blog

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