RTX A6000 48GBOpção econômica48 GB VRAM (+16)
A
Adds memory headroom for longer context windows and future model growth.37.2 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$4,650 MSRP
Can Gemma 2 27B run on NVIDIA V100 32GB?
YES — With Offload
B70Good
Estimated from fit model
Gemma 2 27B needs ~32.1 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~31 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 32.1 GB, 32.6 tok/s, Runs with offload (needs ~0.1 GB host RAM)
32.1 GB required32.0 GB available
100% VRAM needed
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
Memory breakdown
Weights16.5 GB
KV Cache11.2 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsGemma 2 27B on NVIDIA V100 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 32.6 tok/s decode · 5.9s TTFT (warm) · 82 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 36.6 tok/s | 2884 ms | 8K |
| Coding | B | Runs with offload | 31.1 tok/s | 6232 ms | 8K |
| Agentic Coding | F | Too heavy | 18.9 tok/s | 14870 ms | 8K |
| Reasoning | B | Runs with offload | 31.1 tok/s | 7365 ms | 8K |
| RAG | F | Too heavy | 18.9 tok/s | 18587 ms | 8K |
Quantization options
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 | 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 |
Get started
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bOpções de upgrade
Hardware que roda bem Gemma 2 27B
RTX A6000 48GBOpção econômicaRTX PRO 5000 Blackwell 48GBMelhor custo-benefício48 GB VRAM (+16)1344 GB/s (+444)
A
Raises estimated decode speed by about 121%.72 tok/s decodificação
Raises estimated decode speed by about 121%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
NVIDIA L40 48GBUpgrade NVIDIA48 GB VRAM (+16)
A
Raises estimated decode speed by about 32%.43 tok/s decodificação
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
~$5,500 MSRP
Frequently asked questions
Can NVIDIA V100 32GB run Gemma 2 27B?
Yes, NVIDIA V100 32GB can run Gemma 2 27B with a B grade (Runs with offload). Expected decode speed: 31.1 tok/s.
How much VRAM does Gemma 2 27B need?
Gemma 2 27B (27B parameters) requires approximately 32.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Gemma 2 27B?
The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.
What speed will Gemma 2 27B run at on NVIDIA V100 32GB?
On NVIDIA V100 32GB, Gemma 2 27B achieves approximately 31.1 tokens per second decode speed with a time-to-first-token of 6232ms using Q4_K_M quantization.
Can NVIDIA V100 32GB run Gemma 2 27B for coding?
For coding workloads, Gemma 2 27B on NVIDIA V100 32GB receives a B grade with 31.1 tok/s and 8K context.
What context window can Gemma 2 27B use on NVIDIA V100 32GB?
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.
What should I upgrade first if Gemma 2 27B feels slow on NVIDIA V100 32GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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