2026-10-11 17:12 UTC

Independent benchmarks will determine whether Mach-1 Additive 35B can fit in roughly 7GB and sustain up to 120 tokens per second on consumer or edge hardware while retaining practically useful model quality.

state: expiredheat: lowuncertainty: highknownscott: mediumlocal-inference open-models inference-economicsSyzygyResearch

What is this?

SyzygyResearch has published Mach-1 Additive 35B as a GGUF model on Hugging Face and, in its launch testimony, describes it as using 1.7 bits per weight, fitting in roughly 7GB, reaching up to 120 tokens per second, and retaining 95% of the original model’s quality. The supplied independent snippets show that local-inference performance varies substantially with hardware, context length, and quantization, but none directly benchmarks Mach-1 Additive 35B. Its headline size, speed, and quality-retention claims therefore remain unverified by the provided material.

Why it matters to Scott

Scott already holds the operative position in Capability Audit and hardware-aware local inference: size, throughput, and quality claims must be tested under representative hardware and workloads rather than accepted from launch testimony. Mach-1 could materially expand what his gamepc local-model substrate can run if independently validated, but the supplied evidence currently adds only an unverified candidate, not a new conclusion.
ip:concept.capability-auditdev:concept.hardware-aware-local-inferencedev:project.gamepcip:concept.quality-velocityradar:concept.extreme-quantizationradar:concept.local-inferenceradar:concept.model-evaluationradar:bonsai-extreme-quantization
queries asked of Scott's wikis
  • ultra-low-bit quantization quality tradeoffs
  • local inference economics and consumer hardware
  • benchmarking sustained throughput versus headline tokens per second
  • open-weight models and edge deployment strategy
  • memory bandwidth constraints for local LLM inference
  • quality-retention claims and independent model evaluation

Measured heat

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How the heat travelled

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Evidence (2) — ⭐ canonical anchor

sourceobjectauthorscorecomments
🟠 redditSyzygyResearch/Mach-1-Additive-35B-GGUF · Hugging Face
LocalLLaMA
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🟧 echo.x ⭐Official launch post: “Today, we're introducing Mach-1 Additive,” described as a 35B model using 1.7 bits per weight, claiming 95% retentionSyzygy Research——

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