RTX 3090 24GBコスパ重視24 GB VRAM (+8)936 GB/s (+648)
B
Raises estimated decode speed by about 200%.96.2 tok/sデコード
Raises estimated decode speed by about 200%.
Adds memory headroom for longer context windows and future model growth.
〜$1,499 MSRP
Can Mistral Nemo 12B run on RTX 2000 Ada 16GB?
YES — Runs Great
B66Good
Estimated from fit model
Mistral Nemo 12B needs ~12.6 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~32 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 12.6 GB, 32.1 tok/s, Runs well
12.6 GB required16.0 GB available
79% VRAM used
Fit status
Runs well
Decode
32.1 tok/s
TTFT
6023 ms
Safe context
39K
Memory
12.6 GB / 16.0 GB
Memory breakdown
Weights7.3 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsMistral Nemo 12B on RTX 2000 Ada 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: 32.1 tok/s decode · 6.0s TTFT (warm) · 80 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 | 32.1 tok/s | 3285 ms | 39K |
| Coding | B | Runs well | 32.1 tok/s | 6023 ms | 39K |
| Agentic Coding | B | Tight fit | 32.1 tok/s | 8761 ms | 39K |
| Reasoning | B | Runs well | 32.1 tok/s | 7118 ms | 39K |
| RAG | B | Tight fit | 32.1 tok/s | 10951 ms | 39K |
Quantization options
How Mistral Nemo 12B (12B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | B61 |
Q3_K_S | 3 | 5.9 GB | Low | B62 |
NVFP4 | 4 | 6.7 GB | Medium | B63 |
Q4_K_M | 4 | 7.3 GB | Medium | B63 |
Q5_K_M | 5 | 8.6 GB | High | B64 |
Q6_KBest for your GPU | 6 | 9.8 GB | High | B63 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Get started
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoアップグレードオプション
Mistral Nemo 12Bを快適に動かすハードウェア
RTX 3090 24GBコスパ重視RTX 4090 24GBコスパ最良24 GB VRAM (+8)1008 GB/s (+720)
B
Raises estimated decode speed by about 250%.112.5 tok/sデコード
Raises estimated decode speed by about 250%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード24 GB VRAM (+8)672 GB/s (+384)
B
Raises estimated decode speed by about 158%.82.9 tok/sデコード
Raises estimated decode speed by about 158%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can RTX 2000 Ada 16GB run Mistral Nemo 12B?
Yes, RTX 2000 Ada 16GB can run Mistral Nemo 12B with a B grade (Runs well). Expected decode speed: 32.1 tok/s.
How much VRAM does Mistral Nemo 12B need?
Mistral Nemo 12B (12B parameters) requires approximately 12.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Mistral Nemo 12B?
The recommended quantization for Mistral Nemo 12B is Q4_K_M, which balances quality and memory efficiency.
What speed will Mistral Nemo 12B run at on RTX 2000 Ada 16GB?
On RTX 2000 Ada 16GB, Mistral Nemo 12B achieves approximately 32.1 tokens per second decode speed with a time-to-first-token of 6023ms using Q4_K_M quantization.
Can RTX 2000 Ada 16GB run Mistral Nemo 12B for coding?
For coding workloads, Mistral Nemo 12B on RTX 2000 Ada 16GB receives a B grade with 32.1 tok/s and 39K context.
What context window can Mistral Nemo 12B use on RTX 2000 Ada 16GB?
On RTX 2000 Ada 16GB, Mistral Nemo 12B can safely use up to 39K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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