Reviews
Hands-on model tests with the hardware, setup, results and evidence needed to reproduce them.
How to read these reviews
Start with the test setup and conditions, then inspect results, failures and reproduction material. Conclusions state their limits so you can judge whether they apply to your device.
Test notes
125B on a 6GB Laptop GPU: Strata 28–34 tok/s vs llama.cpp ~12, but Strata Almost Didn't Run
Qwen3.8 Flash Next (125B, Q2_0) on an RTX 3060 Laptop with 6GB of VRAM and 64GB of RAM. Strata chats at 28–34 tok/s, more than twice llama.cpp, and halves time to first token on a 4K document. But its default config was 383MiB short on VRAM, and it only ran with the MTP draft layer off and 8K context. llama.cpp is slower but still runs 32K.
Read articleHow Much Do Coding Agents Read Before Your Prompt? pi 1.4K, Codex 6.4K, Claude Code 15.8K Tokens
First-request prompt size and processing time for three agents, read from llama-server logs. On a 6GB laptop, Claude Code takes 8.6x longer than pi just to read its own prompt.
Read articleSame Local Model, pi 12/12, Claude Code 7/12: Three Coding Agents on a 6GB GPU
LFM2.5-24B and Qwen3.6-35B on llama.cpp on a 6GB laptop, driven by pi, Codex and Claude Code. pi passed everything and was fastest. Codex passed but made 12 tool-argument errors. Claude Code's system prompt filled half the context, and it reported tasks "done" with an empty folder.
Read articleQwen-Image 2.1 on a 6GB GPU: Same Chinese Text, Different Prompt, 1/6 vs 6/6 Correct
With INT8 weights and CPU offload, all 17 generations succeeded on an RTX 3060 Laptop 6GB, at about 3 minutes per 1024×1024 image. With identical target text, a bare prompt rendered it strictly correct 1/6 times; a prompt spelling out lines and boundaries, 6/6.
Read article24B on a 6GB laptop: tune -ncmoe, three tasks all pass
On an RTX 3060 Laptop (6GB) with 64GB RAM, Liquid AI LFM2.5-24B-A2B (Q4_0) runs locally. Default -ncmoe 40 uses ~1.3GB VRAM; -ncmoe 30 is faster end-to-end and still passes all three tasks—including the bookkeeping case a 35B run once failed. Thinking mode will not stay off.
Read articleAPEX + MTP on a 6 GB laptop: a 39% median generation gain, at what cost?
Across 12 tasks using identical APEX weights, MTP gave a 39% median task-by-task generation-speed gain on my RTX 3060 Laptop (6 GB VRAM, 64 GB RAM). Median peak VRAM rose by 688 MiB; changed answers and retrieval failures are included below.
Read articleSpark-X2.5-4B on a 6 GB laptop: which model gets things done faster?
On one 6 GB laptop, Spark-X2.5-4B fits entirely in VRAM while Qwen3.6-35B-A3B runs out of system memory. After 72 formal runs, speed and accuracy landed in different places: Spark is dozens of times faster on the main tasks, but Qwen passes more of the 24.
Read articlePoetry posters on a 6 GB GPU: about 4 minutes per image, and 8 of 16 fail on text
On a laptop GPU with 6 GB of VRAM, Qwen-Image 2.1 draws verse, scene and figure into one image; what limits it is text accuracy — only 8 of the 16 images got the text completely right.
Read articleA world model on 6 GB of VRAM, and it produces clips
On a laptop GPU with 6 GB of VRAM, a 1.3B world model produces clips. The cost is time — under a second of picture takes more than ten minutes — and the camera is still not very obedient.
Read articleA 35B MoE on 6 GB of VRAM? 64 GB of RAM Made It Work
With enough system RAM, a 35B-class MoE model can be practically usable with 6 GB of VRAM, provided its expert weights stay on the CPU.
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