These scores were compiled from the linked sources. These published scores were not independently tested here. Different prompts, precision, thinking budgets and tools can change results.
MMLU-Pro
31.39 / 100
Knowledge and reasoning across many subjects. Published accuracy; different evaluation settings can change the result.
Benchmark efficiency: 0.758 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.
Test settings: Publisher-reported results for this exact source model. Evaluation settings differ across model cards; the selected GGUF has not been independently retested. Table row: MMLU-PRO (5-shot).
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
These cards say GPQA without naming the variant. Kept separate from confirmed GPQA Diamond results.
Benchmark efficiency: 0.148 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.
Test settings: Publisher-reported results for this exact source model. Evaluation settings differ across model cards; the selected GGUF has not been independently retested. Table row: GPQA (0-shot).
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Context and version evidence: The repository and file name identify the selected model. Revision hashes are shown above when recorded. Tested context length, full hardware configuration and run date are not consistently recorded in this snapshot. Only settings explicitly stated in each benchmark record are known.
The 41.423 GB figure covers model weights in this file. It excludes the context cache, runtime, operating system and any separate multimodal files. It is not a measured RAM requirement or a token-generation speed.
These complete configurations pass the initial weight-size check for the selected 41.42 GB file. Allow extra memory for context and software. Check the model’s runtime notes and your inference software before buying.
Listing evidence: 2026-09-24. Msecore manufacturer catalog and visible page, selected AI 9 HX370 / 64G RAM / 2T SSD (variant 53408088391974), observed 25 Sep 2026 at USD 1670. Store locale was Italy and currency USD. The generic page title describes HX470, but the selected variant explicitly identifies HX370. Dated manufacturer reference only; not an Amazon or US checkout price. Tax and shipping may differ.
Listing evidence: 2026-09-24. GMKtec, EVO-X2 / 64GB / 1TB, recorded 22 Sep 2026; exact ASIN capacity identified 24 Sep. This is a dated reference, not a current Amazon price; tax, shipping and bundle contents may differ.
Memory matches are estimates, not tested compatibility or speed. As an Amazon Associate I earn from qualifying purchases.
Confirm model architecture and quantization support in your runner. The selected weights use 41.423 GB; this excludes context cache, runtime buffers and other programs.
These are the first listed tiers that pass our reserve policy, not measured minimum requirements. System RAM and VRAM remain separate. Use the memory tool to change the reserve for your context and runner.