Today's Local LLM Pick: translategemma:27b on RTX 3090 (2026)
Daily 3090 recommendation for translategemma:27b: moderate performer at 41.3 tok/s, RTX 3090 benchmark data, use-case fit, and local-vs-cloud decision guide.
Fast verdict
translategemma:27b is a moderate-speed general-purpose model on a 24GB RTX 3090 (41.3 tok/s). It is worth testing locally for batch or offline workloads. For real-time interactive use, measure end-to-end latency with your typical prompt length before committing.
translategemma:27b approaches the 24GB boundary at higher quantizations. Consider Q4 or Q5 if you need context headroom on the RTX 3090. It ranks #10 of 18 in throughput among currently measured models on this RTX 3090. The next faster model is nemotron-3-nano:30b (57.0 tok/s, 38% faster). The next slower model is qwen3.6:35b (41.1 tok/s, 0% slower).
The daily goal is simple: help a 3090 owner decide what to download tonight, what to skip, and when a cloud fallback is the better use of time.
Today’s pick
- Model:
translategemma:27b - Category: general-purpose
- Size tier: large
- Performance tier: moderate
- RTX 3090 speed: 41.3 tok/s
- Latency: 3142 ms
- Test time: 2026-04-01T11:53:50Z
- Baseline command:
ollama run translategemma:27bWho should try it
- RTX 3090 owners deciding whether to download
translategemma:27btonight for local experimentation. - Users comparing local inference speed against cloud rental (RunPod, Vast) before committing to a workflow.
- Anyone building a local LLM toolbox who wants a verified baseline for this model.
Who should skip it
- Users who need long-context production stability before a sustained run has been verified.
- Teams whose workload requires predictable p95 latency under concurrency.
- 8GB/12GB GPU owners unless a smaller quantized variant exists.
Watch points
- Workload-specific testing: generic benchmarks do not guarantee performance on your particular use case.
- Context length: always test at your target context length before assuming production readiness.
- Quantization trade-off: lower quantization saves VRAM but may reduce output quality on nuanced tasks.
Verified benchmark anchors
gpt-oss:20b: 156.1 tok/s | latency 1524 ms | test 2026-04-29T05:39:58Zqwen3-coder:30b: 144.7 tok/s | latency 936 ms | test 2026-06-10T06:45:58Zqwen3:8b: 124.6 tok/s | latency 1389 ms | test 2026-06-10T06:45:58Zqwen2.5:14b: 84.0 tok/s | latency 946 ms | test 2026-04-29T05:39:58Zministral-3:14b: 82.0 tok/s | latency 1960 ms | test 2026-06-10T06:45:58Z
RTX 3090 decision guide
- Batch is the sweet spot: translategemma:27b is best for offline/batch jobs where throughput matters more than single-shot latency.
- Test at your context length: moderate-speed models can slow significantly at longer contexts.
- Quantization choice matters: stepping from Q8 to Q4 gains speed but test quality degradation first.
- Cloud fallback plan: if local latency misses your target, use RunPod/Vast for time-sensitive runs.
Comparisons to validate
translategemma:27bvs the next-fastest and next-slowest model in the benchmark feed.translategemma:27bvsgpt-oss:20b— same size tier, 41 vs 156 tok/s.translategemma:27blocal power cost vs A100 rental for the same workload.
Next actions
- Estimate VRAM fit: /en/tools/vram-calculator/
- Model page: /en/models/translategemma-27b-q4/
- Benchmark changelog: /en/benchmarks/changelog/
- Local hardware path: /en/affiliate/hardware-upgrade/
- Cloud fallback: /go/runpod and /go/vast
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