RTX A6000 48GBコスパ重視48 GB VRAM (+16)768 GB/s (+192)
A
Raises estimated decode speed by about 26%.28.2 tok/sデコード
Raises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
〜$4,650 MSRP
Can Gemma 2 27B run on RTX 5000 Ada 32GB?
YES — With Offload
B69Good
Estimated from fit model
Gemma 2 27B needs ~31.8 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~22 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Balanced
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) — 31.8 GB, 22.3 tok/s, Runs with offload
31.8 GB required32.0 GB available
99% VRAM used
Fit status
Runs with offload
Decode
22.3 tok/s
TTFT
8696 ms
Safe context
8K
Memory
31.8 GB / 32.0 GB
Memory breakdown
Weights16.5 GB
KV Cache11.2 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsGemma 2 27B on RTX 5000 Ada 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: 22.3 tok/s decode · 8.7s TTFT (warm) · 56 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 | Runs well | 22.3 tok/s | 4743 ms | 8K |
| Coding | B | Runs with offload | 22.3 tok/s | 8696 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 |
Quantization options
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 |
Get started
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bアップグレードオプション
Gemma 2 27Bを快適に動かすハードウェア
RTX A6000 48GBコスパ重視RTX PRO 5000 Blackwell 48GBコスパ最良48 GB VRAM (+16)1344 GB/s (+768)
A
Raises estimated decode speed by about 100%.44.7 tok/sデコード
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
〜$4,999 MSRP
NVIDIA A40 48GBNVIDIAアップグレード48 GB VRAM (+16)696 GB/s (+120)
A
Adds memory headroom for longer context windows and future model growth.26.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$5,500 MSRP
Frequently asked questions
Can RTX 5000 Ada 32GB run Gemma 2 27B?
Yes, RTX 5000 Ada 32GB can run Gemma 2 27B with a B grade (Runs with offload). Expected decode speed: 22.3 tok/s.
How much VRAM does Gemma 2 27B need?
Gemma 2 27B (27B parameters) requires approximately 31.8 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 RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, Gemma 2 27B achieves approximately 22.3 tokens per second decode speed with a time-to-first-token of 8696ms using Q4_K_M quantization.
Can RTX 5000 Ada 32GB run Gemma 2 27B for coding?
For coding workloads, Gemma 2 27B on RTX 5000 Ada 32GB receives a B grade with 22.3 tok/s and 8K context.
What context window can Gemma 2 27B use on RTX 5000 Ada 32GB?
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.
What should I upgrade first if Gemma 2 27B feels slow on RTX 5000 Ada 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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