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In one Lenovo Yoga 9 15IMH5, a GeForce GTX 1650 Ti Max-Q decoded Gemma 4 E2B at a median 4.14 times the rate of the laptop’s six-core Intel Core i7-10750H. The same test reported a 3.42× GPU lead in prompt prefill and a 3.62× lead end to end. Those are results for this laptop, model, software build, and test protocol—not a general rule that laptop GPUs are always four times faster than CPUs.
What did the ABBA re-test find?
The GPU led on all three reported measures: generating output tokens (decode), processing the prompt before generation (prefill), and the combined end-to-end interval. Decode is especially relevant when judging how quickly a model produces an answer; prefill matters when a user submits a prompt, and end-to-end performance reflects both stages together.
| Measure | Reported GPU advantage | What it describes |
|---|---|---|
| Decode | 4.14× median | Output-token generation rate |
| Prefill | 3.42× median | Processing the input prompt |
| End to end | 3.62× median | Combined prompt processing and generation |
The eight GPU-to-CPU decode ratios in the report ranged from 3.93× to 4.30×. Across the result grid, the CPU decoded at 16.10–18.31 tokens per second and the GPU at 68.22–71.89 tokens per second. These are measurements reported by xbill in a DEV Community article published in 2026, not an independently measured industry statistic. Read the benchmark report.
What hardware and software were compared?
The test used one Lenovo Yoga 9 15IMH5, so its CPU and GPU shared the laptop’s chassis and cooling system. The CPU was an Intel Core i7-10750H with six cores, twelve threads, and AVX2. The GPU was an NVIDIA GeForce GTX 1650 Ti with Max-Q Design and 4096 MiB of memory.
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The machine ran Debian forky/sid with kernel 7.2.6. The reported toolchain was gcc 16.2.0, CUDA 13.4 (V13.4.92), driver 615.71.09, and llama.cpp commit f95b0d9 (build 318). The model was google/gemma-4-E2B-it-qat-q4_0-gguf, described by the author as a 3.35 GB quantization-aware GGUF.
Both devices served the model using llama-server from the same llama.cpp commit. Settings were matched except for -ngl, the GPU-layer-offload setting. Shared settings included an 8192-token context, f16 key/value cache, flash attention, six CPU threads, twelve batch threads, one parallel request, and metrics enabled.
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How was the benchmark run?
The author used an ABBA sequence: CPU, GPU, GPU, CPU. Before each pass, the script waited at least 120 seconds and until the CPU package temperature was no higher than 50 °C and the GPU temperature no higher than 45 °C. Each pass covered four prompt lengths—94, 516, 998, and 1959 tokens—and two output lengths—32 and 128 tokens—with three repeats per combination. Requests ran one at a time. The report says the prompt cache was cold and the page cache warm.
Alternating the order helps expose whether a device’s result depends on being tested first or after the chassis has warmed. In this run, the ABBA aggregate was 4.14× for decode and 3.42× for prefill. The CPU-first results were 4.09× and 3.38×, respectively; GPU-first results were 4.17× and 3.47×. The author characterized the order effect as about 2% of the ratio on this laptop.
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Did thermal drift affect the result?
The reported repeat behavior differed between devices. GPU pass-to-pass decode drift had a median of +0.6%, ranging from 0.0% to +1.3%. CPU drift had a median of −1.1%, ranging from −9.3% to +0.1%. The CPU’s second pass recorded twice as many throttle events as its first, and CPU repeats within a cell spread by as much as 14.05%; GPU repeats spread by no more than 1.14%. That supports describing the CPU readings as more thermally variable in this run, not claiming that GPUs are inherently more stable.
A temperature gate before each pass cannot by itself show what temperatures or clocks did during that pass. A commenter raised that limitation and suggested tracking per-pass variance alongside clocks and temperatures. The concern is a methodological question, not evidence that a thermal event invalidated the reported ratio.
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What does this mean for someone serving Gemma locally?
For interactive use on this particular machine and configuration, the GPU was the faster option. The CPU remains a usable fallback when the GPU is unavailable or occupied, though its measured decode rate was substantially lower in this test. The result does not establish which device is preferable for another laptop, a different model size or quantization, multiple simultaneous requests, or another software build.
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When comparing another benchmark with this one, check whether it matches the factors that can change the outcome:
- Model, quantization, and model size.
- Exact CPU and GPU, including whether they share a laptop cooling system.
- Software commit, build options, and thread settings.
- Prompt and output lengths, along with concurrency.
- Run order and temperature controls.
- Whether it reports decode, prefill, end-to-end latency, and repeat variation.
How strong is the evidence?
This is a single-machine benchmark covering one GGUF model, one llama.cpp commit, one concurrency setting, and eight paired prompt/output combinations. The author mentions an earlier run with a 4.27× decode lead and 3.63× prefill lead, but the commit, thread flags, and run order all changed together. The difference cannot be attributed to any single change, so the two runs are not a controlled before-and-after comparison.
The report links run details, but no independent replication or audit was verified. The author also said replicating the test on a second machine would be interesting but difficult because this laptop is uncommon. The measurements are useful as a carefully specified result for this setup; they do not establish a general GPU-versus-CPU performance ratio.
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