The available benchmark does not show that Qwen’s 8.4 GB model matches or beats Claude. ISTA-DASLab reports a score for the compact Qwen quantization and compares it with the full-precision base model; it does not provide a head-to-head Claude result. That distinction matters if you are deciding whether to run Qwen locally or use Claude for coding.
What the 8.4 GB figure refers to
ISTA-DASLab lists its Qwen3.8-27B GSQ-RCO IQ2_XS quantization at 8.4 GB. This is the listed size of that model build, not a promise that inference requires exactly 8.4 GB of graphics memory. The actual memory footprint also depends on the runtime, context length, and other settings. The available sources do not establish a universal minimum GPU configuration. ISTA-DASLab’s model card gives the model sizes and benchmark table.
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What the coding benchmark reports
On LiveCodeBench v6, ISTA-DASLab reports 76.57 for its 8.4 GB IQ2_XS quantization and 85.71 for the BF16 base model. These are the publisher’s reported evaluation results for quantizations and the base model—not an independent replication or a Qwen-versus-Claude test. The model card is the primary source for both figures.
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The comparison indicates that the compact quantization scored below its BF16 base model on this benchmark. It does not establish how either model would perform across every kind of coding work, nor does it show that a lower score necessarily means a worse fit for a particular project.
#1 Best Overall
Was the 8.4 GB Qwen model tested against Claude?
No direct result between this 8.4 GB build and Claude is provided by the cited benchmark table. Geeky Gadgets and Skalablog also report that the compact build was not directly tested against Claude. That should be read as a limitation of the published comparison, not proof that no private or unpublished test exists.
A meaningful head-to-head would need to identify the exact Qwen and Claude versions, tasks, prompts, tool access, inference settings, compute or token budgets, scoring method, and test date. Without those controls and a reported Claude result, there is no evidence here to declare a winner between them.
Rank #2
What to check before trying Qwen locally
The 8.4 GB file size is a useful starting point, but it is not enough to determine whether the model will run well on your system. Consider the memory available to the model after accounting for the runtime and context, and whether you plan to load any vision components. The cited sources do not provide one hardware setup that is guaranteed to work across runtimes and workloads.
- Check available GPU memory rather than assuming the file will fit because its listed size is 8.4 GB.
- Account for the context length and runtime you intend to use; these affect memory needs.
- Confirm whether your intended setup includes vision components, which may add requirements.
How to interpret the result
If your question is whether the compact Qwen build preserves the BF16 model’s reported LiveCodeBench v6 score, the listed score is lower: 76.57 versus 85.71. If your question is whether it beats Claude, this evidence cannot answer that. Treat the figures as one publisher’s benchmark comparison of Qwen variants, not a general ranking of coding assistants.
Quick Recap
Rank #4
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