PC Slower Than It Used to Be?
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteIn one reported test serving google/gemma-4-E2B-it with a hand-written pure-JAX implementation, an AWS g6.2xlarge delivered 48.4 decode tokens per second, compared with 12.9 on a g5g.2xlarge—about 3.7 times the measured decode throughput. That is a result for this model, code and configuration, not a general speed guarantee for other models or serving stacks.
What the benchmark compared
The test, published by xbill for AWS Community Builders on August 31, 2026, used spot instances and the same byte-identical payload on both machines. It served google/gemma-4-E2B-it through a hand-written pure-JAX port, without PyTorch, vLLM or torch_xla. The build was 51bc52c9e2e9, the configuration was ple4 + int8_lm_head, and each instance used tpu_jax_weight_bytes of 6,155,450,950. The benchmark report gives the setup and measurements.
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| Instance | Host and GPU reported | Decode gauge throughput |
|---|---|---|
g5g.2xlarge |
Graviton2, aarch64; NVIDIA T4G, Turing, SM 7.5 | 12.9–13.0 tokens/s across listed prompt sweeps |
g6.2xlarge |
x86_64; NVIDIA L4, Ada, SM 8.9; run in us-east-1d | 48.3–48.5 tokens/s across listed prompt sweeps |
The headline calculation uses 48.4 divided by 12.9, or approximately 3.7. The author describes it as “3.7x on decode, for the same code and the same weights.” Identical payload and configuration make the comparison informative, but they do not make it a GPU-only experiment: the host architecture and base image differed as well.
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Decode speed is not end-to-end speed
The decode gauge isolates token generation throughput; the end-to-end rates in the report were lower and declined as input prompts grew. The report lists these measurements:
| Input prompt length | g5g end-to-end rate | g6 end-to-end rate |
|---|---|---|
| 41 tokens | 12.43 tokens/s | 46.23 tokens/s |
| 521 tokens | 11.28 tokens/s | 42.87 tokens/s |
| 2,057 tokens | 8.22 tokens/s | 34.57 tokens/s |
| 3,593 tokens | 27.55 tokens/s |
The author attributes the end-to-end decline to prefill—the work of processing the input prompt—rather than to a comparable drop in the relatively stable decode gauge. The 3.7× headline therefore should not be read as an end-to-end rate for every prompt length.
Why the author says g5g lagged
For this serving implementation, the author’s profiler analysis attributed 87% of g5g decode to dtype conversion and an fp32 path, versus 0% on g6. The report also put g5g at 26% of its memory-bandwidth roofline and g6 at roughly 100%. These are the author’s interpretations of this profile, not universal characteristics of every workload on those instance families.
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Tensor Core utilization was reported as zero on both systems, which the author said was unexplained. That matters when interpreting the result: the observed gap was not accompanied by reported Tensor Core use on either side, and the profiler explanation is specific to the tested code path.
How strong is the evidence?
The report describes one run per instance, with three repeats per prompt-sweep cell and medians. The g5g profile was reproduced on a second instance; the g6 profile was measured once. This is useful benchmark evidence, but limited replication and multiple platform differences mean it does not conclusively isolate the GPU as the sole cause or establish expected performance elsewhere.
The report also says a 4,105-token prompt served on g5g with MAX_MODEL_LEN=4096, while a 5,120-token prompt failed during a prefill transient. Treat this as a behavior of that test setup, not a platform-wide context limit.
What the result does—and does not—tell you about cost
The benchmark author explicitly did not measure or claim instance price or price per token. Higher measured throughput alone cannot establish which option is cheaper for a deployment: that requires current prices and a comparable workload, along with latency and throughput measurements.
A separate AWS SageMaker benchmark illustrates a broader comparison approach by holding model, serving container and workload consistent across its tested configurations and reporting throughput, latency and cost per output token for its own workloads. Those results concern different tests and do not supply a cost figure for this g5g-versus-g6 run. AWS’s SageMaker benchmark is useful as an example of the additional dimensions to measure, not as a direct price comparison here.
How to apply the comparison to a deployment
Use the 3.7× result as a reason to benchmark your own workload, not as a capacity-planning multiplier. For a fair decision, keep the model, weights, serving code, precision, prompt and output lengths, concurrency and measurement method consistent. Record both decode and end-to-end throughput, plus latency and current regional instance pricing; verify that the required instance type is available and that your account has the necessary quota.
AWS maintains the current EC2 accelerated-computing instance specifications. Check live regional availability, quotas and prices before deploying: the benchmark’s spot capacity and reported g6 location do not establish what will be available or cost-effective in another region or at another time.
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