Gemma 2 27B needs ~31.8 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~21 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs with offload
Decode
22.3 tok/s
TTFT
8696 ms
Safe context
8K
Memory
31.8 GB / 32.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 22.3 tok/s | 4743 ms | 8K |
| Coding | B | Runs with offload | 21.2 tok/s | 9131 ms | 8K |
| Agentic Coding | F | Too heavy | 9.0 tok/s | 31459 ms | 8K |
| Reasoning | B | Runs with offload | 22.3 tok/s | 10277 ms | 8K |
| RAG | F | Too heavy | 9.0 tok/s | 39324 ms | 8K |
How Gemma 2 27B (27B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B66 |
Q3_K_S | 3 | 13.2 GB | Low | B67 |
NVFP4 | 4 | 15.1 GB | Medium | B68 |
Q4_K_M | 4 | 16.5 GB | Medium | B69 |
Q5_K_M | 5 | 19.4 GB | High | B69 |
Q6_KBest for your GPU | 6 | 22.1 GB | High | B68 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bUpgrade-Optionen
RTX A6000 48GBGünstige WahlRaises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,650 MSRP
RTX PRO 5000 Blackwell 48GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,999 MSRP
NVIDIA A40 48GBNVIDIA-UpgradeAdds memory headroom for longer context windows and future model growth.
ca. $5,500 MSRP
Yes, RTX 5000 Ada 32GB can run Gemma 2 27B with a B grade (Runs with offload). Expected decode speed: 21.2 tok/s.
Gemma 2 27B (27B parameters) requires approximately 31.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.
On RTX 5000 Ada 32GB, Gemma 2 27B achieves approximately 21.2 tokens per second decode speed with a time-to-first-token of 9131ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on RTX 5000 Ada 32GB receives a B grade with 21.2 tok/s and 8K context.
On RTX 5000 Ada 32GB, Gemma 2 27B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-27b-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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