RTX 5070 12GB经济之选12 GB VRAM (+1)672 GB/s (+56)
B
This setup is broadly balanced for this model.93.3 tok/s 解码
~$549 MSRP
Can Dolphin 2.9 8B run on RTX 2080 Ti 11GB?
YES — Runs Great
B57Good
Estimated from fit model
Dolphin 2.9 8B needs ~8.8 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~88 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) — 8.8 GB, 88.2 tok/s, Runs well
8.8 GB required11.0 GB available
80% VRAM used
Fit status
Runs well
Decode
88.2 tok/s
TTFT
2195 ms
Safe context
33K
Memory
8.8 GB / 11.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsDolphin 2.9 8B 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: 88.2 tok/s decode · 2.2s TTFT (warm) · 221 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 | 88.2 tok/s | 1197 ms | 33K |
| Coding | B | Runs well | 88.2 tok/s | 2195 ms | 33K |
| Agentic Coding | C | Runs with offload | 88.2 tok/s | 3193 ms | 33K |
| Reasoning | B | Runs well | 88.2 tok/s | 2594 ms | 33K |
| RAG | C | Runs with offload | 88.2 tok/s | 3991 ms | 33K |
Quantization options
How Dolphin 2.9 8B (8B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C51 |
Q3_K_S | 3 | 3.9 GB | Low | C52 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_M | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | C53 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | C53 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run Dolphin 2.9 8B on your machine.
Run
ollama run dolphin-llama3升级选项
能流畅运行 Dolphin 2.9 8B 的硬件
Frequently asked questions
Can RTX 2080 Ti 11GB run Dolphin 2.9 8B?
Yes, RTX 2080 Ti 11GB can run Dolphin 2.9 8B with a B grade (Runs well). Expected decode speed: 88.2 tok/s.
How much VRAM does Dolphin 2.9 8B need?
Dolphin 2.9 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.
What is the best quantization for Dolphin 2.9 8B?
The recommended quantization for Dolphin 2.9 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Dolphin 2.9 8B run at on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, Dolphin 2.9 8B achieves approximately 88.2 tokens per second decode speed with a time-to-first-token of 2195ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run Dolphin 2.9 8B for coding?
For coding workloads, Dolphin 2.9 8B on RTX 2080 Ti 11GB receives a B grade with 88.2 tok/s and 33K context.
What context window can Dolphin 2.9 8B use on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, Dolphin 2.9 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
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