Today's Local LLM Pick: gemma3:27b on RTX 3090 (2026)

Fast verdict

gemma3:27b runs at 39.7 tok/s on a 24GB RTX 3090 — in the deliberate range. This model prioritizes quality or parameter count over raw speed. Test it on offline or background tasks first, and consider a smaller quantization if interactive response time matters.

gemma3:27b 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 translategemma:27b (41.3 tok/s, 4% faster). The next slower model is qwen2.5-coder:32b (37.6 tok/s, 5% 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: gemma3:27b
Category: general-purpose
Size tier: large
Performance tier: deliberate
RTX 3090 speed: 39.7 tok/s
Latency: 3093 ms
Test time: 2026-08-05T05:23:10Z
Baseline command:

ollama run gemma3:27b

Who should try it

RTX 3090 owners deciding whether to download gemma3:27b tonight 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 2026-04-29T05:39:58Z
qwen3-coder:30b: 144.9 tok/s | latency 1012 ms | test 2026-08-12T04:15:51Z
qwen3:8b: 123.5 tok/s | latency 1456 ms | test 2026-08-12T04:15:51Z
qwen2.5:14b: 84.0 tok/s | latency 946 ms | test 2026-04-29T05:39:58Z
ministral-3:14b: 81.2 tok/s | latency 1989 ms | test 2026-08-12T04:15:51Z

RTX 3090 decision guide

Offline first: prioritize gemma3:27b for scheduled batch inference, research, or validation workflows.
Context is the bottleneck: reduce context to the minimum viable length for your task.
Quantize before you buy hardware: Q4 or Q5 may make this viable on 24GB where Q8 is not.
Cloud for interactive: if real-time response is required, treat gemma3:27b as a cloud-fallback candidate.

Comparisons to validate

gemma3:27b vs the next-fastest and next-slowest model in the benchmark feed.
gemma3:27b vs gpt-oss:20b — same size tier, 40 vs 156 tok/s.
gemma3:27b local power cost vs A100 rental for the same workload.

Next actions

Estimate VRAM fit: /en/tools/vram-calculator/
Model page: /en/models/gemma3-27b-q4/
Benchmark changelog: /en/benchmarks/changelog/
Local hardware path: /en/affiliate/hardware-upgrade/
Cloud fallback: /go/runpod and /go/vast

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