The Problem
The Spark Arena leaderboard does an excellent job measuring tokens/sec. But speed without quality is an incomplete picture. When choosing between MXFP4, FP8, INT4, and NVFP4 quants of the same base model, the community currently has no standardized way to answer the question that actually matters:
How much quality did I trade for that speed gain?
Existing benchmarks like MMLU and HumanEval give you static scores, but they don’t capture the specific failure modes of quantized models — compounding reasoning errors, long context degradation, and most importantly, sycophantic drift under adversarial pressure.
The Proposal
An escape room style benchmark purpose-built for quantization quality comparison on DGX Spark hardware.
Two quants of the same base model are placed in an escape room simultaneously. Six rooms, each a different reasoning challenge. To advance, a model must solve the current room correctly. The chain is sequential — errors compound, just like they do in real workloads.
But here is the twist that makes this novel: both models can see each other’s answers before submitting their final response.
This adversarial layer tests something no static benchmark does — whether a quantized model maintains confidence in correct reasoning when pressured by a wrong answer from its opponent. Degraded quants don’t just get answers wrong. They get uncertain. They get easier to bully. This benchmark exposes exactly that.
The Six Rooms
Room 1 — The Cipher A simple encoded message. Decode and state the hidden word. Baseline instruction following. Nearly every quant passes. Establishes the run.
Room 2 — The Map A text-based graph described in prose. Find the path from entry to exit. Tests spatial reasoning and state tracking. The model must hold a mental map across multiple paragraphs.
Room 3 — The Mechanism A broken code snippet. Fix it so the test suite passes. The orchestrator executes the code. Pass/fail is fully deterministic — no LLM judge, no ambiguity.
Room 4 — The Archive A 20k+ token document with a specific obscure detail buried deep inside. That detail is the key. Long context recall is one of the first capabilities to degrade under aggressive quantization.
Room 5 — The Paradox A multi-step logic puzzle with contradictory surface clues. One consistent answer exists. Tests whether reasoning chains hold under pressure.
Room 6 — The Warden The final room. The model is given everything it learned across rooms 1–5 and must synthesize it into a single answer. A direct test of whether information persists and compounds correctly across a full context window.
The Adversarial Turn
After both models submit their initial answer to a room, each is shown the other’s response and given one opportunity to revise or hold. The orchestrator records both decisions.
This produces four outcome states per model per room:
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Held Ground — was correct, stayed correct under pressure
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Manipulated — was correct, got bullied into a wrong answer
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Self-Corrected — was wrong, updated correctly after seeing opponent
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Trapped — was wrong, stayed wrong
Manipulated is the critical signal. A model that gets talked out of a correct answer by a confident wrong opponent is exhibiting exactly the kind of degradation that matters in agentic and multi-step workloads.
The Scoring
Each run produces a single comparable output:
Base score: +10 per room escaped
Held Ground: +5
Manipulated: -8
Self-Corrected: +3
Trapped: 0
Retry penalty: -2 per retry
Speed bonus: +1 per 10s under par time
The BF16 version of each model is run first to establish a baseline score. Every quant is then expressed as a quality retention percentage relative to that baseline. So the community output becomes:
Qwen3-Coder-Next INT4 AutoRound — Escaped 6/6 — 71.2 tok/s — 96.4% quality retention vs BF16
That is a meaningful, actionable number. Not just “it scored 78 on MMLU.”
The Technical Stack
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Python asyncio — both endpoints called simultaneously, adversarial turns managed in sequence
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Pydantic — strict schema validation for every room’s expected answer format
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Room registry — each room is a self-contained class with a prompt, validator, and par time
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SQLite — every run, turn, and answer stored for full reproducibility
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FastAPI — exposes results and triggers new runs via API
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Live view — watch the game play out room by room in real time as both models compete
Why This Matters for the Spark Community Specifically
The DGX Spark sits in a unique position — enough memory and compute to run large models at multiple quantization levels on a single node. The hardware makes the quant choice a real decision that every Spark owner faces. MXFP4 vs FP8 vs INT4 is not a theoretical question here. It has a real answer that depends on your workload.
This benchmark gives the community a shared, reproducible, intuitive way to measure that tradeoff. The leaderboard already ranks nodes by speed. This adds the quality dimension that completes the picture.
Open Questions for the Community
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Which base models should be the standard test subjects?
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Should room difficulty scale based on model size?
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Should the adversarial bluff be configurable — e.g. the orchestrator intentionally feeds a confident wrong answer to stress test further?
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Who wants to help build it?
This proposal came out of a conversation about what tokens/sec doesn’t tell you. The escape room framing isn’t just aesthetic — sequential dependency and adversarial pressure are the two conditions where quantization degradation shows up most clearly in production. The benchmark is designed to find exactly that signal.