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ISTA-DASLab · MODEL RECORD

Qwen3.8-27B-GSQ-RCO-GGUF

Qwen3.8-27B-GSQ-RCO-GGUF has 26.896 billion total parameters. The selected IQ3_XXS GGUF file is 10.094 GB.

Data snapshot: . 3 published tests in this catalog.

Model file and details

Provider
ISTA-DASLab
Total parameters
26,895,998,464
Exact file size
10,094,357,632 bytes
Quantization / precision
IQ3_XXS
Weight repository
ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF
Original model repository
ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF
Weight revision
d562806dbafae37109975e970aae91b43e73b440
Model revision
d562806dbafae37109975e970aae91b43e73b440
Runtime notes
Confirm model architecture and quantization support in your runner.
Recorded release date
2026-08-28
Date evidence
Source repository createdAt (release-date proxy)

Qwen3.8-27B-GSQ-RCO-IQ3_XXS.gguf

GGUF file and download on Hugging Face · Original model card

Published benchmark results

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.

GPQA Diamond

88.89 / 100

Hard science questions. This is a science reasoning test, not an overall intelligence score.

Benchmark efficiency: 8.806 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 GSQ-RCO IQ3_XXS row, 3.00 bits per weight. AIME25, GPQA-Diamond and LiveCodeBench v6. These results are specific to this quantized variant.

Published quantization-specific result for GSQ-RCO IQ3_XXS.

Read the GPQA Diamond source

LiveCodeBench v6

84.57 / 100

Solving coding problems. Version 6 only; published pass rates can vary with generation settings.

Benchmark efficiency: 8.378 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 GSQ-RCO IQ3_XXS row, 3.00 bits per weight. AIME25, GPQA-Diamond and LiveCodeBench v6. These results are specific to this quantized variant.

Published quantization-specific result for GSQ-RCO IQ3_XXS.

Read the LiveCodeBench v6 source

AIME 2025

100 / 100

Competition math from AIME 2025. Published answer accuracy.

Benchmark efficiency: 9.907 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 GSQ-RCO IQ3_XXS row, 3.00 bits per weight. AIME25, GPQA-Diamond and LiveCodeBench v6. These results are specific to this quantized variant.

Published quantization-specific result for GSQ-RCO IQ3_XXS.

Read the AIME 2025 source

Memory and comparison limits

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 10.094 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.

Use the model ranking chart to compare one test at a time. Use the 8, 16 and 32 GB memory guide. Check hardware memory and bandwidth before choosing a model.

Need a computer for Qwen3.8-27B-GSQ-RCO-GGUF?

These complete configurations pass the initial weight-size check for the selected 10.09 GB file. Allow extra memory for context and software. Check the model’s runtime notes and your inference software before buying.

CPU inference

Glorlin Ryzen 7 6800H Mini PC

Ryzen 7 6800H, Radeon 680M, 16GB LPDDR5-6400, 512GB SSD

$469.99Dated reference · not a live offer

16 GB system RAM · CPU planning

The LPDDR5 memory is listed with a 16GB maximum; no RAM upgrade is assumed.

Check price on Amazon ↗Specifications & model limits
Listing and price evidence

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.

Apple unified memory

Apple Mac mini M5 Pro · 24GB

15-core CPU, 16-core GPU, 24GB unified memory, 512GB SSD

$1,699.00Dated reference · not a live offer

24 GB unified memory

Fixed 24GB memory and internal storage limit model size; no CUDA support; Amazon main-offer price and stock were not verified in this run.

Check price on Amazon ↗Specifications & model limits
Listing and price evidence

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.

AMD unified memory

GMKtec EVO-X2

Ryzen AI Max+ 395, 64GB LPDDR5X-8000, 1TB PCIe 4.0 SSD

$2,199.99Dated reference · not a live offer

64 GB unified memory

Memory is soldered and shared with the operating system.

Check price on Amazon ↗Specifications & model limits
Listing and price evidence

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.

Compare more computers for this model

RAM and VRAM planning for Qwen3.8 27B GSQ-RCO

Confirm model architecture and quantization support in your runner. The selected weights use 10.094 GB; this excludes context cache, runtime buffers and other programs.

System RAM (CPU inference)
16 GB planning tier · 11.2 GB initial weight budget
Dedicated VRAM
16 GB planning tier · 12.8 GB initial weight budget
Unified memory
16 GB planning tier · 11.2 GB initial weight budget

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.

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