Can 8 GB or 16 GB run Gemma4-12B QAT Uncensored Balanced?
The files fit inside both example pools, but 8 GB does not establish a usable full-offload configuration. This derivative model newly entered the bounded top-100 GGUF activity scan. Hugging Face showed 936,840 downloads and 399 likes when checked on . Those counts are activity signals, not search volume or unique users.
Exact files at revision ae8045a. Remaining-space figures are arithmetic, not measured runtime requirements.
Package
Exact files
Left in 8 GiB
Left in 16 GiB
Text model onlyText chat and generation
7.38 GB / 6.87 GiB
1.13 GiB
9.13 GiB
Text + vision projectorImage input
7.56 GB / 7.04 GiB
0.96 GiB
8.96 GiB
Text + MTP draftPublisher-documented speculative decoding
7.64 GB / 7.11 GiB
0.89 GiB
8.89 GiB
Text + vision + MTPBoth optional features
7.81 GB / 7.27 GiB
0.73 GiB
8.73 GiB
Formula: pool GiB − file bytes ÷ 1,073,741,824. Even text-only leaves just 1.13 GiB in an 8 GiB pool; all three files leave 0.73 GiB. The runtime, context cache, display use, operating system and other processes still need memory.
What has actually been measured
One community benchmark recorded 26.4 tokens/s and 1,133 ms time to first token with LM Studio, thinking enabled and a 32,768-token context on an AMD Radeon RX 7600 XT reporting 15 GB available GPU memory. This is a measured result for that run, not an estimate for other hardware. One community-submitted run on one system. The page does not identify driver, backend, exact offload settings, KV-cache precision or every host component; it does not predict another 16 GB GPU or system-memory computer. Review the run and reproduction settings.
Compatibility checks before buying
Use the exact pinned Q4_K_M model card and files. The separate 0.18 GB projector is required for image input; the 0.25 GB draft head is optional.
The publisher lists llama.cpp, LM Studio, Jan, koboldcpp. It also reports LM Studio tensor-split crashes on multi-GPU setups; that warning does not establish compatibility with every single-GPU backend.
The publisher claims roughly 60% faster generation with MTP in llama.cpp, but did not supply enough run-level detail for a comparable benchmark. Treat it as a publisher claim, not a speed forecast.
The benchmark cards below describe Google's base Gemma 4 12B model. They are not measurements of this uncensored derivative and must not be transferred to it.
Hardware comparison: the ASUS ROG NUC 16 catalog record has 64 GB system RAM and a separate 16 GB RTX 5080 Laptop GPU. This model has not been tested here on that computer. Check the exact Amazon configuration; price and stock are not verified.
As an Amazon Associate I earn from qualifying purchases.
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
77.2 / 100
Knowledge and reasoning across many subjects. Published accuracy; different evaluation settings can change the result.
Benchmark efficiency: 10.84 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.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Hard science questions. This is a science reasoning test, not an overall intelligence score.
Benchmark efficiency: 11.065 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 Diamond.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Solving coding problems. Version 6 only; published pass rates can vary with generation settings.
Benchmark efficiency: 10.11 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: LiveCodeBench v6.
Published point estimate, not an independent head-to-head evaluation. Quantization and inference settings can change results.
Competition math from AIME 2026. Published answer accuracy.
Benchmark efficiency: 10.882 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: AIME 2026 no tools.
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 7.122 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 7.12 GB file. Allow extra memory for context and software. Check the model’s runtime notes and your inference software before buying.
Listing evidence: 2026-09-25. Amazon.com base price visible in an owner-supplied screenshot reviewed 25 Sep 2026; capture time not supplied. Dated reference, not a live quote. Excludes card signup offers, coupons, tax and shipping.
Listing evidence: 2026-10-03. Apple US launch price announced 22 September 2026: USD 1,699. Dated manufacturer reference, not an Amazon offer; 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 7.122 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.