Can Qwen3.6-35B-A3B on an RTX 3060 Replace Google Vision for Receipt-to-JSON Extraction?
Summary
A developer shares their experience using a local Qwen VL model on an RTX 3060 to parse Japanese receipts into JSON, replacing Google Vision, with results showing accurate extraction of key fields at ~31 seconds per receipt.
Similar Articles
Wow! Qwen 3.6:35b-a3b on a 3090... pretty amazing.
A user shares impressive results running a quantized Qwen 3.6:35b-a3b model on a used RTX 3090, achieving 160 tokens per second output after fitting the model into VRAM, and demonstrates vision capabilities with a 75-second video processing time.
Is Qwen3-VL-2B the only viable VLM for JSON extraction on a "potato"?
The author claims Qwen3-VL-2B is the only viable vision-language model for JSON extraction on low-end hardware, outperforming larger models like Qwen3-VL-4B, yet it is absent from major benchmarks.
I pushed Qwen3.8-27B to 99 tps single request and 1150 tps with a batch request on a RTX 3090
The author optimized the Qwen3.8-27B model inference on an RTX 3090 GPU, achieving up to 99 tokens per second for single requests and 1150 tps with batch processing through various quantization and optimization techniques, and released the updated code on GitHub.
Tried Qwen3.6-27B-UD-Q6_K_XL.gguf with CloudeCode, well I can't believe but it is usable
User reports surprisingly usable coding performance from Qwen3-27B-UD-Q6_K_XL.gguf running locally on RTX 5090 at ~50 tok/s with 200K context, marking a significant leap in local model quality.
@TheAhmadOsman: Qwen 3.8 27B in NVFP4 would fit on a single RTX 5090 btw
The Qwen 3.8 27B model has been released in an NVFP4 quantized version, enabling it to run on a single RTX 5090 GPU with enhancements in coding, agentic tasks, and vision-language understanding.