Translategemma 27B Q8 VRAM Requirement and Ollama Model Size
Translategemma 27B Q8 needs 24GB minimum VRAM, 34GB optimal VRAM, and 64GB+ system RAM for a safer Ollama run. Auto-discovered from local Ollama inventory.. Caveat: Auto-generated family metadata; review for taxonomy accuracy..
Hardware Snapshot
| Family | Translategemma |
|---|---|
| Scenario | chat |
| License scope | open-source |
| Quantization | Q8 |
| VRAM minimum | 24GB |
| VRAM optimal | 34GB |
| Best local GPU | RTX 6000 Ada 48GB |
| Cloud fallback | A100 80GB |
| Updated | 2026-02-24 |
| Data status | Verified by Real Hardware |
| Ollama source | Library reference (verified: 2026-02-24) |
| Ollama tag | translategemma:27b |
| Category | chat |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 7.9 |
| RTX 4090 24GB | 10.7 |
| A100 80GB | 19 |
Quick Answers
- How much VRAM does Translategemma 27B Q8 need?
- Translategemma 27B Q8 needs about 24GB minimum VRAM and 34GB optimal VRAM for a safer local run target.
- Can Translategemma 27B Q8 run on an RTX 3090 24GB?
- It may load on an RTX 3090 24GB because the minimum estimate is 24GB, but the safer target is 34GB. Expect tighter context or lower throughput.
- What is the Ollama command for Translategemma 27B Q8?
- Use ollama run translategemma:27b. Check the Ollama tag translategemma:27b before running.
- What is the approximate Ollama model size for Translategemma 27B Q8?
- Translategemma 27B Q8 uses the translategemma:27b Ollama tag. Treat the Q8 27 profile as smaller than the runtime VRAM budget; use 24GB minimum VRAM and 34GB optimal VRAM as the safer sizing numbers.
- How much system RAM does Translategemma 27B Q8 need in Ollama?
- Plan for at least 64GB system RAM alongside 24GB minimum VRAM and 34GB optimal VRAM. More RAM helps if layers spill to CPU or if you run multiple models.
- What is the RAM vs VRAM requirement for Translategemma 27B Q8?
- Translategemma 27B Q8 should be sized by GPU VRAM first: 24GB minimum and 34GB optimal. System RAM should be at least 64GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is Translategemma 27B Q8 VRAM usage on an RTX 3090?
- Translategemma 27B Q8 is borderline for an RTX 3090 24GB target. The page estimates 24GB minimum and 34GB optimal VRAM, with spill risk marked as high without a larger GPU or cloud.
- Which LocalVRAM profiles share the translategemma:27b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: Translategemma 27B Q8 (Q8, 24GB min/34GB optimal); Translategemma 27B Q4 (Q4, 18GB min/28GB optimal); Translategemma 27B Q5 (Q5, 20GB min/30GB optimal); Translategemma 27B FP16 (FP16, 30GB min/42GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is Translategemma 27B Q8 benchmark data measured or estimated?
- Translategemma 27B Q8 has a verified RTX 3090 result of 41.293 tokens/s from LocalVRAM benchmark data.
Ollama Model Size and RAM Notes
| Ollama tag | translategemma:27b |
|---|---|
| Run command | ollama run translategemma:27b |
| Model size guidance | Q8 quantization, 27 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 24GB minimum, 34GB optimal |
| System RAM planning | 64GB or more when running this tag locally with Ollama |
Download size alone is not a safe runtime budget. Runtime memory also includes KV cache, context length, and GPU layer placement.
Ollama RAM vs VRAM Requirements
| GPU VRAM usage estimate | 24GB minimum / 34GB optimal |
|---|---|
| System RAM requirement | 64GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Borderline |
| CPU spill risk | High without larger GPU or cloud |
| Context warning | Longer context increases KV cache memory, so real VRAM usage can exceed short-prompt estimates. |
For Ollama, VRAM is the first fit check. System RAM matters when layers spill to CPU, when context grows, or when the local app stack runs beside the model.
Available Tag Variants and VRAM Targets
| Profile | Quantization | VRAM target | Ollama tag |
|---|---|---|---|
| Translategemma 27B Q8 | Q8 | 24GB min / 34GB optimal | translategemma:27b |
| Translategemma 27B Q4 | Q4 | 18GB min / 28GB optimal | translategemma:27b |
| Translategemma 27B Q5 | Q5 | 20GB min / 30GB optimal | translategemma:27b |
| Translategemma 27B FP16 | FP16 | 30GB min / 42GB optimal | translategemma:27b |
Ollama library tags often group multiple quantization choices under one model family. LocalVRAM separates them into VRAM profiles so you can pick the right local target.
Real Hardware Benchmark (RTX 3090)
| Tokens/s | 41.293 |
|---|---|
| Latency | 3142 ms |
| Prompt tokens | 30 |
| Eval tokens | 96 |
| Test time | 2026-04-01T11:53:50Z |
| GPU model | NVIDIA GeForce RTX 3090 |
Verified by real hardware.
Performance Curve
Reference anchors are baseline estimates. Measured RTX 3090 data is overlaid when available.
Best Hardware for Translategemma 27B Q8
- Local run: RTX 3090 (24GB) (Check latest deal) for around 41.293 tok/s on this profile.
- Cloud run: RunPod A100 80GB , about 0.5x the local 3090 speed anchor.
- Alternative cloud: Vast.ai options for flexible spot pricing.
Local vs Cloud Cost Hint
| Mode | 40h / month | 120h / month |
|---|---|---|
| Local power only (3090 baseline) | $2.24 | $6.72 |
| A100 80GB | $78 | $234 |
Related Model Profiles
- Translategemma 27B Q4 18GB min, 28GB optimal
- Translategemma 27B Q5 20GB min, 30GB optimal
- Translategemma 27B FP16 30GB min, 42GB optimal
ollama run translategemma:27b More chat models More 30b-34b models Benchmark changelog Submit your test result Run on RunPod Try Vast.ai We may earn a commission if you click links on this page.