RX 7900 XT 20GBコスパ最良
20 GB VRAM (+4)800 GB/s (+224)
B
Raises estimated decode speed by about 29%.69.6 tok/sデコード
Raises estimated decode speed by about 29%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can Gemma 2 9B run on RTX 5000 Ada Laptop 16GB?
YES — Runs Great
B70Good
Estimated from fit model
Gemma 2 9B needs ~13.1 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~54 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 13.1 GB, 53.8 tok/s, Runs well
13.1 GB required16.0 GB available
82% VRAM used
Fit status
Runs well
Decode
53.8 tok/s
TTFT
3597 ms
Safe context
8K
Memory
13.1 GB / 16.0 GB
Memory breakdown
Weights5.5 GB
KV Cache5.1 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsGemma 2 9B on RTX 5000 Ada Laptop 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 53.8 tok/s decode · 3.6s TTFT (warm) · 135 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 53.8 tok/s | 1962 ms | 8K |
| Coding | B | Runs well | 53.8 tok/s | 3597 ms | 8K |
| Agentic Coding | C | Very compromised (needs ~0.7 GB host RAM) | 30.6 tok/s | 9195 ms | 8K |
| Reasoning | B | Runs well | 53.8 tok/s | 4251 ms | 8K |
| RAG | C | Very compromised (needs ~0.7 GB host RAM) | 30.6 tok/s | 11494 ms | 8K |
Quantization options
How Gemma 2 9B (9B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B62 |
Q3_K_S | 3 | 4.4 GB | Low | B63 |
NVFP4 | 4 | 5.0 GB | Medium | B63 |
Q4_K_M | 4 | 5.5 GB | Medium | B64 |
Q5_K_M | 5 | 6.5 GB | High | B65 |
Q6_K | 6 | 7.4 GB | High | B66 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | B66 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2アップグレードオプション
Gemma 2 9Bを快適に動かすハードウェア
Frequently asked questions
Can RTX 5000 Ada Laptop 16GB run Gemma 2 9B?
Yes, RTX 5000 Ada Laptop 16GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 53.8 tok/s.
How much VRAM does Gemma 2 9B need?
Gemma 2 9B (9B parameters) requires approximately 13.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Gemma 2 9B?
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Gemma 2 9B run at on RTX 5000 Ada Laptop 16GB?
On RTX 5000 Ada Laptop 16GB, Gemma 2 9B achieves approximately 53.8 tokens per second decode speed with a time-to-first-token of 3597ms using Q4_K_M quantization.
Can RTX 5000 Ada Laptop 16GB run Gemma 2 9B for coding?
For coding workloads, Gemma 2 9B on RTX 5000 Ada Laptop 16GB receives a B grade with 53.8 tok/s and 8K context.
What context window can Gemma 2 9B use on RTX 5000 Ada Laptop 16GB?
On RTX 5000 Ada Laptop 16GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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