llama.cpp merged a /v1/systemone endpoint on October 2 that returns typed answers with probabilities from five open model families running locally. Whether it can replace hosted classification calls depends on calibration that each team has to measure on its own data.
Perspective Coverage
3 publishers
- Builder
- Builder 48%
- Operator
- Operator 29%
- Investor
- Investor 23%
Reality
- Evidence50
- Adoption15
- Hype gap+5
- Incentives55
- Confidence55
Two arms on the same laptop differ by one flag. The small card wins because llama.cpp leaves Gemma 4's 1.93 GB per-layer embedding table in mmap and pulls a few rows per token, so only about 1.08 GB of body is resident.
Reality
- Evidence64
- Adoption12
- Hype gap+5
- Incentives20
- Confidence58
Per-tensor layout maps now drive his GGUF releases. The sensitivity data behind them came out of more than 1,000 quantizations of two Qwen3.5 models, scored by KL divergence against bf16 on wikitext-2-raw.
Reality
- Evidence57
- Adoption28
- Hype gap+8
- Incentives42
- Confidence55
Treating the runtime and the weights as a container image buys you a hardened default docker run and a signable artifact. On Apple Silicon, though, that same boundary costs you the GPU. One hands-on run puts a number on that cost.
Reality
- Evidence52
- Adoption20
- Hype gap−5
- Incentives30
- Confidence48
Hugging Face counts 28,531 community GGUF conversions of Alibaba's Qwen models against 54 from Alibaba itself. Procurement signs for the model; production loads the artifact.
Reality
- Evidence60
- Adoption71
- Hype gap+14
- Incentives55
- Confidence58