Problemsby GlyphLLM responses are too slow for users
7 posts from 7 people6d activeSteady, +1 this week
Posts per day
The posts behind it
Extremely slow response on Antigravity Language Service (daily-* test endpoint) & production endpoint failing
`replace_strict` slow streaming on concatted frames
### Checks - [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the [latest version](https://pypi.org/project/polars/) of Polars. ### Reproducible example ### Log output ### Issue description I had some workload that went...
Codex Desktop feels significantly slower on October 26: slow streaming and task execution
## Summary Codex Desktop feels significantly slower than usual on October 26, 2026. This affects both the pace of visible text generation and the time needed to carry out tasks. The experience feels rate-limited, but I have no evidence that account throttling is the cause. ##...
GPU acceleration detection and configuration in Ollama UI
...GPU: Intel Arc 140T (16 GB reported) - Intel driver: 32.0.101.8860 - Model: Qwen3 4B ## Workaround After investigating, I manually enabled the following environment variables: After restarting Ollama, `ollama ps` reported: **Before:** 100% CPU **After:** 100% GPU The GPU was...
A 24KB stand-alone HTML-LLM that can generate consistent stories
...format of this LLM. The embeddings had some room though.. It's mathematically impossible to save space with 1 bit quants or less with the LittleBit approach here, as the model matrices are just too small for that - the gains would get eaten up by the overhead of the correction...
[Feature]: Graph tokens per second over time by model and provider
### Check for existing issues - [x] I have searched the existing issues and checked that my issue is not a duplicate. ### The Feature An operator who sends one model to more than one provider can see daily token volume and average response time for that model, and spend totals...
It Fit in Memory and Was Still Unusable - Do the Bandwidth Arithmetic First
A 15GB model loaded fully onto the GPU and generated at 5.6 tokens per second. The theoretical ceiling was 8. Capacity and throughput are set by different resources, and the obvious fix for a tight fit - shrink the model - barely moves the one that matters. One division tells...
Companies and products named
About this problem
Evidence
7 posts from 7 people in 7 places, about 1 a week over 63 days. Mostly on DEV #machinelearning, BerriAI/litellm, r/LocalLLaMA. Tools named alongside: Capacity, Ollama, ASUS, Clear.
Frustration Frustration 2 of 3· Seen on GitHub issues, DEV, Reddit, Community forums
How it was grouped
Posts that state a pain and match the "llm-latency" rule. First post Sep 6, 2026, latest Nov 7, 2026. Corroborated: 3 or more posts from 2 or more people or places. Method
The same problem elsewhere
Claude responses are too slow
2 posts from 2 people on GitHub issues, Hacker News. Unverified.
History