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In one reported test serving google/gemma-4-E2B-it with a hand-written pure-JAX implementation, an AWS g6.2xlarge produced 48.4 decode tokens per second versus 12.9 on a g5g.2xlarge—about 3.7 times the measured decode throughput. That is a result for this model, implementation, configuration, and test setup, not a general promise that g6 will serve every LLM 3.7 times faster.
What the benchmark compared
The benchmark author used spot instances and the same byte-identical payload on both: build 51bc52c9e2e9, configuration ple4 + int8_lm_head, and tpu_jax_weight_bytes of 6,155,450,950. The model was google/gemma-4-E2B-it, served using a hand-written pure-JAX port without PyTorch, vLLM, or torch_xla. The figures below are the author’s reported measurements, not independently reproduced results. Read the benchmark and its setup.
| Measure or setup | g5g.2xlarge | g6.2xlarge |
|---|---|---|
| GPU | NVIDIA T4G, Turing, SM 7.5 | NVIDIA L4, Ada, SM 8.9 |
| Host architecture | Graviton2, aarch64 | x86_64 |
| Reported decode gauge throughput across prompt sweeps | 12.9–13.0 tokens/s | 48.3–48.5 tokens/s |
| Headline decode comparison | 12.9 tokens/s | 48.4 tokens/s; approximately 3.7× g5g |
The g6 measurement was run in us-east-1d. The common model payload and configuration make this a useful observed comparison, but they do not make it a controlled test of the GPU alone: the host architectures and base images differed as well.
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The headline compares decode gauge rates. End-to-end rates include prompt prefill, so they were lower and declined as the input prompt grew. The author attributes that decline to prefill processing. These reported rates show why a decode number by itself may not predict the speed a user experiences on requests with long prompts.
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| Input prompt length | g5g end-to-end | g6 end-to-end |
|---|---|---|
| 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 benchmark reports a g6 end-to-end rate at 3,593 input tokens but no matching g5g value for that prompt length. The reported decode gauge stayed comparatively stable across the listed sweeps; end-to-end throughput did not.
Why the author says g5g lagged
For this pure-JAX serving implementation, the author’s profiler analysis attributed 87% of g5g decode time to dtype conversion and an fp32 path, compared with 0% on g6. The author also reported g5g reaching 26% of its memory-bandwidth roofline and g6 roughly 100%. These are the author’s interpretations of this run and implementation, not general performance characteristics for every workload on those instance families.
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Tensor Core utilization was reported as zero on both GPUs; the author said the reason was unexplained. That result is a reminder that having accelerator hardware does not guarantee a particular serving stack will use every relevant feature efficiently.
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How strong is the 3.7× conclusion?
It is a meaningful result for deciding what to test next, but not conclusive evidence of a universal g6 advantage of that size. The author reports one run per instance, with three repeats per sweep cell and medians. The g5g profile was reproduced on a second instance; the g6 profile was measured once. Alongside the GPU, the machine architecture and base image differed.
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For a deployment decision, test the exact model and serving stack you plan to run. Keep the workload and software configuration consistent, and record both decode and end-to-end throughput. Include prompt length, output length, precision, concurrency, latency, region, and repeated runs in the comparison; otherwise, a faster decode gauge may not translate to faster or cheaper production requests.
What the test says about context length
On g5g, the author used MAX_MODEL_LEN=4096. A prompt of 4,105 tokens served, while a 5,120-token prompt failed on a prefill transient. This is an observation from the described test, not a platform-wide context limit or guarantee for other configurations.
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Does g6 cost less per token?
The benchmark did not measure instance price or price per token, and its throughput result cannot answer that question by itself. Cost depends on current pricing and the workload’s useful output, latency, utilization, and deployment conditions. AWS’s separate SageMaker comparison illustrates the additional measurements needed by reporting throughput, latency, and cost per output token for its own tested models and workloads; those results do not establish the cost or value of this EC2 g5g/g6 test. See AWS’s separate SageMaker benchmark.
What to verify before choosing an instance
Use the benchmark as a reason to run your own workload test, then confirm the deployment details that can change over time. AWS’s EC2 accelerated-computing instance specifications are the official reference for instance family details. Check current regional availability, account quotas, and prices for your intended region and purchase option: the benchmark’s spot capacity and region do not establish what will be available or cost-effective elsewhere.
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