RTX 3090 24GBOpción económica24 GB VRAM (+4)936 GB/s (+576)
C
Sube la velocidad estimada de decodificación alrededor de un 133%.119.3 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 133%.
~$1,499 MSRP
Can HelpingAI2 9B run on RTX 4000 Ada 20GB?
YES — Runs Great
C50Usable
Estimated from fit model
HelpingAI2 9B needs ~9.7 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~51 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) — 9.7 GB, 51.1 tok/s, Runs well
9.7 GB required20.0 GB available
49% VRAM used
Fit status
Runs well
Decode
51.1 tok/s
TTFT
3785 ms
Safe context
172K
Memory
9.7 GB / 20.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsHelpingAI2 9B on RTX 4000 Ada 20GB
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: 51.1 tok/s decode · 3.8s TTFT (warm) · 128 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 | C | Runs well | 51.1 tok/s | 2065 ms | 172K |
| Coding | C | Runs well | 51.1 tok/s | 3785 ms | 172K |
| Agentic Coding | C | Runs well | 51.1 tok/s | 5506 ms | 172K |
| Reasoning | C | Runs well | 51.1 tok/s | 4473 ms | 172K |
| RAG | C | Runs well | 51.1 tok/s | 6882 ms | 172K |
Quantization options
How HelpingAI2 9B (9B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C45 |
Q3_K_S | 3 | 4.4 GB | Low | C46 |
NVFP4 | 4 | 5.0 GB | Medium | C46 |
Q4_K_M | 4 | 5.5 GB | Medium | C47 |
Q5_K_M | 5 | 6.5 GB | High | C47 |
Q6_K | 6 | 7.4 GB | High | C48 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C50 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run HelpingAI2 9B on your machine.
Run
lms load hf-bartowski--helpingai2-9b-gguf && lms server startOpciones de mejora
Hardware que ejecuta bien HelpingAI2 9B
RTX 3090 24GBOpción económicaRTX 4090 24GBMejor relación calidad-precio24 GB VRAM (+4)1008 GB/s (+648)
C
Sube la velocidad estimada de decodificación alrededor de un 147%.126 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 147%.
~$1,599 MSRP
RTX PRO 4000 Blackwell 24GBMejora NVIDIA24 GB VRAM (+4)672 GB/s (+312)
C
Sube la velocidad estimada de decodificación alrededor de un 101%.102.8 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 101%.
~$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run HelpingAI2 9B?
Yes, RTX 4000 Ada 20GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 51.1 tok/s.
How much VRAM does HelpingAI2 9B need?
HelpingAI2 9B (9B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.
What is the best quantization for HelpingAI2 9B?
The recommended quantization for HelpingAI2 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will HelpingAI2 9B run at on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, HelpingAI2 9B achieves approximately 51.1 tokens per second decode speed with a time-to-first-token of 3785ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run HelpingAI2 9B for coding?
For coding workloads, HelpingAI2 9B on RTX 4000 Ada 20GB receives a C grade with 51.1 tok/s and 172K context.
What context window can HelpingAI2 9B use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, HelpingAI2 9B can safely use up to 172K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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