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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsIn a September 2026 benchmark, Gemma 4 E2B, E4B, and 12B decoded on a SageMaker NVIDIA T4 at 0.77–0.82 times the single-request speed reported for an L4 run. At 16 parallel requests, the T4 delivered 0.53–0.63 times the reported throughput. The T4 and L4 returned byte-for-byte identical outputs on the benchmark’s 40-question check, but that small test does not establish general quality equivalence.
What the T4-versus-L4 benchmark actually compared
The figures come from a benchmark author’s one-account deployments: the T4 runs used SageMaker ml.g4dn.xlarge in us-east-2 on September 30, 2026; the L4 figures came from a related benchmark run the previous day on ml.g6.xlarge. This was not a controlled, same-day hardware comparison: the T4 used vLLM 0.30.0 in an AWS container modified with a Turing patch, FP16, and a driver-580 host image, while the L4 used the stock container and BF16. The author measured single-request decode, throughput at up to 16 parallel requests, and answers to 40 questions at temperature zero. Throughput was measured using the AWS CLI from one client machine. Read the benchmark and its methodology.
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| Model | T4 single-request decode | L4 single-request decode | T4/L4 ratio | T4/L4 throughput ratio at 16 requests |
|---|---|---|---|---|
| E2B | 108.5 tokens/s | 141.7 tokens/s | 0.77 | 0.63 |
| E4B | 65.6 tokens/s | 79.9 tokens/s | 0.82 | 0.57 |
| 12B | 28.5 tokens/s | 35.0 tokens/s | 0.81 | 0.53 |
These are the benchmark author’s reported values, not guarantees for other regions, software versions, request shapes, or traffic patterns. The single-request ratios support the shorthand “about 0.8x,” but they do not predict performance under concurrent load: in this test the T4’s relative throughput was lower at 16 requests.
Did both GPUs produce the same answers?
For each of E2B, E4B, and 12B, the author reports that the T4 outputs matched the corresponding L4 outputs byte for byte across 40 questions at temperature zero. The quality table recorded 36 correct answers out of 40 for E2B and E4B, and 40 out of 40 for 12B. Those scores describe that question set and evaluation; matching those outputs does not show that the models are equivalent on a broader task mix or that small quality differences would be detected.
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Which Gemma 4 sizes fit on one T4?
The benchmark author served E2B, E4B, and 12B on a single T4. A 26B-A4B attempt loaded the weights but then failed with an out-of-memory error, making 12B the largest model in this tested configuration that served successfully. This is a configuration-specific result, not a general hardware compatibility rule.
For 12B, the author reports a T4 KV cache of 20,354 tokens, equivalent to about 2.48 requests at the configured 8,192-token context length. The related L4 table reports a larger KV cache for each tested model size. Cache capacity affects how much context and concurrent work can fit; check memory headroom for your own context length and concurrency rather than assuming the single-request decode rate answers that question.
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How do hourly price and cost per output token compare?
The benchmark author reported on-demand SageMaker prices from an AWS Price List API lookup for us-east-2 on September 30, 2026. The cost-per-million-output-token figures below are the author’s calculations from those prices and measured throughput at 16 parallel requests; they are not current universal rates or a forecast for different workloads.
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| Instance | Reported hourly price in us-east-2 | E2B cost per million output tokens | E4B cost per million output tokens | 12B cost per million output tokens |
|---|---|---|---|---|
ml.g4dn.xlarge (T4) |
$0.736/hour | $0.260 | $0.419 | $0.945 |
ml.g6.xlarge (L4) |
$1.1267/hour | $0.249 | $0.369 | $0.760 |
In those reported figures, the T4 had the lower hourly rate, while the L4 had the lower calculated cost per million output tokens for all three models at 16-way concurrency. The practical choice depends on utilization: a lower hourly charge can suit light or intermittent traffic, while the benchmark’s measured steady-load cost favored the L4. Check AWS’s current prices and regional capacity before planning deployment economics.
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What should you verify before reproducing the setup?
- Container and GPU support: The benchmark attributes T4 support to a Turing patch in a derived vLLM container. Do not assume the stock container supports the same configuration.
- Driver and AMI: The author says the CUDA 13 container on
ml.g4dnrequiresInferenceAmiVersionset to the driver-580 host. Confirm the current SageMaker API, host image, and container compatibility before applying this version-sensitive guidance. - Model and memory fit: Validate the exact model variant, context length, and expected parallel requests. In the reported setup, 26B-A4B ran out of memory on one T4.
- Workload match: Compare single-request latency separately from throughput at your expected concurrency, and check cost at that load. The 0.8x single-request ratio alone does not describe a busy endpoint.
- Benchmark comparability: The T4 and L4 runs differed in date, container, precision, and deployment, so treat their ratios as reported results rather than a hardware-only measurement.
Does SageMaker JumpStart offer an official Gemma 4 deployment path?
AWS announced on April 29, 2026, that Gemma 4 E4B, 26B-A4B, and 31B were available in SageMaker JumpStart, deployable through SageMaker Studio or the SageMaker Python SDK. That announcement establishes a JumpStart path for those named variants; it does not establish that the benchmark’s custom T4 container and driver configuration is a supported JumpStart deployment. AWS also notes E4B audio input capability. See AWS’s JumpStart announcement.
A separate AWS Builder Center article evaluates Gemma 4 quantization-aware-training formats on SageMaker L4 instances in us-east-2 with vLLM 0.30.0. It reports that a setup with 4-bit embeddings and lm_head decoded 1.12–1.39 times faster than its compared 16-bit-embeddings setup and matched answers on that article’s test for the sizes shown. Those results concern a different benchmark and do not independently validate the T4-versus-L4 comparison. Read the separate L4 quantization benchmark.
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