new article

1 files changed, 26 insertions, 23 deletions

diff --git a/content/posts/an-update-on-local-models.md b/content/posts/new-tests-around-local-llms.md
index a916bb5..77e446c 100644
--- a/content/posts/an-update-on-local-models.md
+++ b/content/posts/new-tests-around-local-llms.md
@@ -1,9 +1,12 @@
 ---
-title: ""
-draft: true
-date: 2026-08-22
-lastmod:
-description:
+title: "New tests around local LLMs"
+draft: false
+date: 2026-08-26
+lastmod: 2026-08-26
+description: |
+  Qwen 3.8 27B is an exciting release, but still too large
+  for common dev machines. But I can find interesting models
+  to run locally.
 categories:
   - LLM
   - Coding Agents
@@ -11,8 +14,7 @@ categories:
 
 ## Qwen 3.8 27B
 
-This week was interesting for local models, because we had the
-open weights release of Qwen 3.8 27B. It was much expected, because
+This week was interesting for local models with the open-weights release of Qwen 3.8 27B. It was highly anticipated, because
 Qwen 3.6 held the crown for a long time in open models of a size
 that is runnable on almost consumer hardware.
 
@@ -20,37 +22,38 @@ The [Artificial Analysis](https://artificialanalysis.ai/models/qwen3-8-27b)
 score is pretty damn good! 52 with xhigh is in the ballpark of 
 GPT-5.6 Luna (max) and DeepSeek V4 Flash 0731, which were my go
 to models for august, given their relatively low price.
-To think that I could run as a powerful model locally feels
+To think that I could run as powerful a model locally feels
 insane.
 For reference, it sits between a Claude Opus 4.6 and a
-Claude Opus 4.7, and that was the best what we had in February
+Claude Opus 4.7, and that was the best available in February
 in Claude Code.
 
 ## Artificial Analysis pareto frontier
 
-https://artificialanalysis.ai is my reference for model comparison.
+[Artificial Analysis](https://artificialanalysis.ai) is my reference for model comparison.
 It actually compiles scores from many benchmarks. I know there are
-many pitfalls to these benchmarks, but hey, we have to reduce
+many pitfalls to these benchmarks, but hey, I have to reduce
 the world's complexity, right?
-I found my prefered charts, and that's when you remove all models.
+I found my preferred charts when removing all models.
 Then AAI highlights the pareto frontier, with the best option
-at each tradeoff. Go see for yourself!
+at each tradeoff.
 
 
 [Int vs Cost/Time per task](https://artificialanalysis.ai/models#intelligence-comparisons) - What frontier model to use in
-what case. Here we can see Luna is pretty neat for cost, and
+what case. Here Luna looks pretty neat for cost, and
 that's what I used in august.
 
 [Int vs Active/Total params](https://artificialanalysis.ai/models#model-size) - This helps me understand what I could
-run on my hardware. For this one, you need to filter on small
-(and maybe tiny) models. This show Qwen 3.8 27B as much better
+run on my hardware. For this one, filtering on small
+(and maybe tiny) models helps. This shows Qwen 3.8 27B as much better
 than the previous Gemma 4 26B A4B. And I can see a new contender
 [Ling 3.0 Tiny](https://artificialanalysis.ai/models/ling-3-0-tiny)
 that looks promising.
 
 ## Running modern models
 
-I wonder if local inference got better since https://blog.kagamino.dev/posts/local-llm-inference-offline-scenario/.
+I wonder if local inference got better since my
+[previous attempt](/posts/local-llm-inference-offline-scenario/).
 Back then, I had decent inference performance with Gemma 4 E2B
 (AAI 15), but it felt too dumb to do meaningful work.
 I wanted to test Qwen 3.5 9B, but this time it was too slow,
@@ -63,12 +66,12 @@ or Ling ? Or better inference, like oMLX on Mac?
 
 Last time was a one shot scenario, so this time I want to test
 a coding agentic workflow with Pi - it has a smaller system prompt.
-I'll go by feel, because I don't have clear metrics to target.
+I go by feel, because I don't have clear metrics to target.
 
 Sadly, Qwen 3.8 27B is too large, even with oMLX. That's also
-because I need a larger context of 100k tokens. And I couln't
-run Ling 3.0 Tiny, because of unsupported architecture. So much
-for modern models!
+because I need a larger context of 100k tokens. And I couldn't
+run Ling 3.0 Tiny, because of unsupported architecture on
+both oMLX and LMStudio. So much for modern models!
 
 ## New contenders
 
@@ -80,7 +83,7 @@ than the
 [Gemma 4 E2B](https://artificialanalysis.ai/models/gemma-4-e2b/)
 (AAI 10) I tested last time.
 
-With oMLX and a 8bits version, it runs using around 10GB
+With oMLX and an 8-bit version, it runs using around 10GB
 unified memory. The first prompt is always a bit slow, but then
 the KV cache kicks in and it feels snappy enough.
 But after some time it ended up in a locked grep loop. I think
@@ -90,7 +93,7 @@ I had an attempt on my desktop, which is better a prefill (input)
 than the Mac. But this time it didn't feel really smart.
 
 In the end, I'm a bit disappointed I couldn't run the newer models,
-even though Ling 3.0 Tiny was supposed to fit. But maybe i can
+even though Ling 3.0 Tiny was supposed to fit. But maybe I can
 run it later when the tooling has caught up.
 I think Qwen 3.5 4B may have a spot for offline use, as I can run
 it at 50 tok/s on Mac. I'd like to investigate using it