The $2k Laptop That Replaced My $200/Month AI Subscription
5 comments
Have you tried other local models?
The 14B Q4_K_M needs 9GB, but Q3_K_M is 7.3GB. But you also need some room for context. Still, maybe using `--override-tensor` in llama.cpp would get you a 50% improvement over "naively" offloading layers to the GPU. Or possibly GPT-OSS-20B. It's 12.1GB in MXFP4, but it’s a MOE model so only a part of it would need to be on the GPU. On my dedicated 12GB 3060 it runs at 85 t/s, with a smallish context. I've also read on Reddit some claims that Qwen3 4B 2507 might be better than 8B, because Qwen never released a "2507" update for 8B.
The 14B Q4_K_M needs 9GB, but Q3_K_M is 7.3GB. But you also need some room for context. Still, maybe using `--override-tensor` in llama.cpp would get you a 50% improvement over "naively" offloading layers to the GPU. Or possibly GPT-OSS-20B. It's 12.1GB in MXFP4, but it’s a MOE model so only a part of it would need to be on the GPU. On my dedicated 12GB 3060 it runs at 85 t/s, with a smallish context. I've also read on Reddit some claims that Qwen3 4B 2507 might be better than 8B, because Qwen never released a "2507" update for 8B.
Haven't tried GPT-OSS-20B yet — the MOE approach is interesting for keeping VRAM usage down while getting better reasoning. 85 t/s on a 3060 is impressive. I'll look into that.
I've been on Qwen3 8B mostly because it was "good enough" for the mechanical stages (scanning, scoring, dedup) and I didn't want to optimize the local model before validating the orchestration pattern itself. Now that the pipeline is proven, experimenting with the local model is the obvious next lever to pull.
The Qwen3 4B 2507 claim is interesting — if the quality holds for structured extraction tasks, halving the VRAM footprint would open up running two models concurrently or leaving more room for larger contexts. Worth testing.
Thanks for the pointers — this is exactly the kind of optimization I haven't had time to dig into yet.
I've been on Qwen3 8B mostly because it was "good enough" for the mechanical stages (scanning, scoring, dedup) and I didn't want to optimize the local model before validating the orchestration pattern itself. Now that the pipeline is proven, experimenting with the local model is the obvious next lever to pull.
The Qwen3 4B 2507 claim is interesting — if the quality holds for structured extraction tasks, halving the VRAM footprint would open up running two models concurrently or leaving more room for larger contexts. Worth testing.
Thanks for the pointers — this is exactly the kind of optimization I haven't had time to dig into yet.
The hybrid local/cloud orchestration pattern is a smart way to manage costs, especially for pipelines with distinct stages. We built a similar routing logic into https://simplio.dev to handle the fallback and provider switching automatically, which saves a lot of custom pipeline code.
How is quality for what Qwen 8B provides compares to proprietary models? Is it good enough for your use case?
For the mechanical stages (scanning, scoring, dedup) — indistinguishable from proprietary models. These are structured tasks: "score this post 1-10 against these criteria" or "extract these fields from this text." An 8B model handles that fine at 30 tok/s on consumer GPU.
For synthesis and judgment — no, it's not close. That's exactly why I route those stages to Claude. When you need the model to generate novel connections or strategic recommendations, the quality gap between 8B and frontier is real.
The key insight is that most pipeline stages don't need synthesis. They need pattern matching. And that's where the 95% cost savings live.
For synthesis and judgment — no, it's not close. That's exactly why I route those stages to Claude. When you need the model to generate novel connections or strategic recommendations, the quality gap between 8B and frontier is real.
The key insight is that most pipeline stages don't need synthesis. They need pattern matching. And that's where the 95% cost savings live.
Cost for a 50-item research pipeline: $0.15-0.40 vs $8-15 all-cloud. Same output quality where it matters.
Stack: RTX 5080 laptop, Ollama in Docker with GPU passthrough, PostgreSQL, Redis, Claude API for the final 20%.
The pattern: scan locally → score locally → deduplicate locally → synthesize via cloud. Four stages, three are free.
Gotchas I hit: Qwen3's thinking tokens through /api/generate (use /api/chat instead), Docker binding to IPv4 only while Windows resolves localhost to IPv6, and GPU memory ceilings on consumer cards.
Happy to share architecture details in comments.