local-llm
- One rebuild moved gpt-oss off ROCm. Two models stayed
Part 6 sent gpt-oss-120b to ROCm because Vulkan prefill collapsed at depth. One llama.cpp rebuild later it is 3.1x faster at 64k; two models stay on ROCm.
- Every way my GPU job queue went quiet
A file-based job queue ran one GPU box unattended for six days. It never crashed. Here are six ways it went quiet, or nearly did, and the fixes.
- How to tell when your coding agent lied, not just failed
Grade what a coding agent says, not only its code: buggy code is a bug, promised code that doesn't exist is a lie. The script, the rule, the checker's traps.
- Seven FAILs, one setting: rerunning agents at 262144 tokens
Seven agent cells on the A9 failed at a 131072-token context. Rerun at 262144, six passed. Four of the seven never needed the extra room.
- Tabby turned file contents into numbers
TabbyAPI's qwen3_coder parser runs every tool argument through json.loads and strip(). A string "123" arrives as 123, and file content loses its edges.
- The models deleted the benchmark's log
Coding agents found the harness's live log in their workspace, read it as another agent's run, and deleted it. Three rows recorded zero tool calls.
- LFM2.5 is fast. Gemma 4 finishes the job
LFM2.5-24B-A2B vs Gemma 4 26B-A4B as local coding agents, 3 runs per task: LFM wins on speed; on Spring Boot only Gemma wrote tests.
- 2.2x faster prefill, 1.04x faster agent
A new engine prefilled 2.1-2.2x faster than llama.cpp at every depth. Driving a coding agent, it finished 4% sooner. The server logs say where the speed went.
- The engine got credit for the quant
open-jet beat llama.cpp 7 to 3 on a Zig agent task. On open-jet's own GGUF, llama.cpp went 7 of 9 too. Only the speed gap belonged to the engine.
- My benchmark served the model at half speed
The launcher turned on MTP drafting for five models. The benchmark's own server script didn't, and its result rows had no field that could show it.