niklaslenzDeepSeek V4.1 Flash in the Gauntlet
2 DGX Sparks 256 GB · TensorFold native (Zig 0.17.0), Mia's recipe @0e7d421e (DeepSeek-V4.1-Flash-1017, EXL3 EAQ 2.9 bpw, DSpark drafts, tensor parallel 2)
What happened on the course
Each range is one task with one answer and no retry. Every wrong or missing target is a miss and costs a penalty loop. The race time is thinking, running, shooting and loops together.
| # | Task | Targets | Hits | Thinking | Buzzer | Penalty loops | Range time |
|---|---|---|---|---|---|---|---|
| 1 | River ferry (puzzle, level 1) | ✕✕✕✕✕ | 0 / 5 | 4.1 s | no | 5 (60.0 s) | 71.3 s |
| 2 | Painter (code, level 2) | ●●●●✕ | 4 / 5 | 3.4 s | no | 1 (12.0 s) | 22.6 s |
| 3 | Bench seating (puzzle, level 3) | ●●●●● | 5 / 5 | 4.0 s | no | 0 (0.0 s) | 11.2 s |
| 4 | Snake (code, level 4) | ●✕●✕● | 3 / 5 | 11.8 s | no | 2 (30.0 s) | 49.0 s |
● hit, ✕ miss. The range time runs from arriving at the mat to leaving the range, loops included. A buzzer means the thinking cap ran out and the game forced the answer.
Every number has its file
Result (result.json) Race log (log.jsonl) Model answers (io.jsonl) System prompt Exact command Verify output Model config
Version: prompt version gauntlet-4, tasks bia-2, rules 1, prompt fingerprint 07fff686, tasks fingerprint c2c0f4f6, forced-answer style notes. Verified: the replay of the race log reproduces the end state and every code answer re-runs to the same verdict. Forced-answer style: how the game asks for the answer when the buzzer sounds (a prefilled answer start, or notes when the engine refuses that). Command and config are normalized: internal addresses appear as 127.0.0.1.
- Run on a different pair of identical DGX Sparks than the page measurement. The recipe, image, mode and settings are the same. Only the start preparation differs: the start check for free memory was set to 111 GiB instead of the default 112 GiB, the guard script before the start was set to 110 GiB instead of 112 GiB, and the file cache was emptied before the start in a different way. This affects only the start, not how the server runs.
Who made what
- Recipe and EXL3 quant (EAQ 2.9 bpw)Mia's AI LabX @MiaAI_labGitHubHugging FaceWebsite
- ModelDeepSeekHugging FaceX @deepseek_aiGitHubWebsite
- Engine (TensorFold)Ash HartX @ashxhartGitHubHugging FaceWebsite
- DeepSeek-V4.1 support in TensorFoldBertholomus AIX @bertholomusaiGitHubHugging Face
- EXL3 formatturboderpX @turboderp_Hugging FaceGitHub
Full v2 results of the matching setup (same model, engine, quant and hardware; the engine build of the race is not recorded in the race files) · How the game test works · All races