Gemma 2 27B needs ~32.1 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~33 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
0.1 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
32.6 tok/s
TTFT
5935 ms
Safe context
8K
Memory
32.1 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 | Tight fit | 38.4 tok/s | 2747 ms | 8K |
| Coding | A | Runs with offload (needs ~0.1 GB host RAM) | 32.6 tok/s | 5935 ms | 8K |
| Agentic Coding | F | Too heavy | 18.9 tok/s | 14870 ms | 8K |
| Reasoning | A | Runs with offload (needs ~0.1 GB host RAM) | 32.6 tok/s | 7014 ms | 8K |
| RAG | F | Too heavy | 19.9 tok/s | 17702 ms |
How Gemma 2 27B (27B params) fits at each quantization level on NVIDIA V100 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 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 91.2 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 76.6 tok/s |
Yes, NVIDIA V100 32GB can run Gemma 2 27B with a A grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 32.6 tok/s.
Gemma 2 27B (27B parameters) requires approximately 32.1 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 NVIDIA V100 32GB, Gemma 2 27B achieves approximately 32.6 tokens per second decode speed with a time-to-first-token of 5935ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on NVIDIA V100 32GB receives a A grade with 32.6 tok/s and 8K context.
On NVIDIA V100 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-27b-on-v100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
| 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 |
| Qwen3-VL 30B A3B Instruct | 30B | S | 94.3 tok/s |
| Qwen 3.5 35B A3B | 35B | S | 83.3 tok/s |
| Qwen 3 32B | 32B | S | 33.6 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.