RTX 5070 12GB经济之选12 GB VRAM (+1)672 GB/s (+56)
B
This setup is broadly balanced for this model.77.1 tok/s 解码
~$549 MSRP
Can HelpingAI2 9B run on RTX 2080 Ti 11GB?
YES — Runs Great
B56Good
Estimated from fit model
HelpingAI2 9B needs ~8.8 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~73 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 8.8 GB, 72.9 tok/s, Runs well
8.8 GB required11.0 GB available
80% VRAM used
Fit status
Runs well
Decode
72.9 tok/s
TTFT
2655 ms
Safe context
49K
Memory
8.8 GB / 11.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsHelpingAI2 9B on RTX 2080 Ti 11GB
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: 72.9 tok/s decode · 2.7s TTFT (warm) · 182 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 72.9 tok/s | 1448 ms | 49K |
| Coding | B | Runs well | 72.9 tok/s | 2655 ms | 49K |
| Agentic Coding | C | Tight fit | 72.9 tok/s | 3861 ms | 49K |
| Reasoning | B | Runs well | 72.9 tok/s | 3137 ms | 49K |
| RAG | C | Tight fit | 72.9 tok/s | 4826 ms | 49K |
Quantization options
How HelpingAI2 9B (9B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C50 |
Q3_K_S | 3 | 4.4 GB | Low | C52 |
NVFP4 | 4 | 5.0 GB | Medium | C52 |
Q4_K_M | 4 | 5.5 GB | Medium | C52 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C51 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
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 start升级选项
能流畅运行 HelpingAI2 9B 的硬件
RTX 5070 12GB经济之选RTX 3080 12GB最佳性价比12 GB VRAM (+1)912 GB/s (+296)
B
Raises estimated decode speed by about 73%.126 tok/s 解码
Raises estimated decode speed by about 73%.
~$799 MSRP
RTX 3080 Ti 12GBNVIDIA 升级12 GB VRAM (+1)912 GB/s (+296)
B
Raises estimated decode speed by about 69%.122.9 tok/s 解码
Raises estimated decode speed by about 69%.
~$1,199 MSRP
Frequently asked questions
Can RTX 2080 Ti 11GB run HelpingAI2 9B?
Yes, RTX 2080 Ti 11GB can run HelpingAI2 9B with a B grade (Runs well). Expected decode speed: 72.9 tok/s.
How much VRAM does HelpingAI2 9B need?
HelpingAI2 9B (9B parameters) requires approximately 8.8 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 2080 Ti 11GB?
On RTX 2080 Ti 11GB, HelpingAI2 9B achieves approximately 72.9 tokens per second decode speed with a time-to-first-token of 2655ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run HelpingAI2 9B for coding?
For coding workloads, HelpingAI2 9B on RTX 2080 Ti 11GB receives a B grade with 72.9 tok/s and 49K context.
What context window can HelpingAI2 9B use on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, HelpingAI2 9B can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--helpingai2-9b-gguf-on-rtx-2080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: