MacBook Pro M4 Pro 24GB经济之选
24 GB Unified (+8)
C
This setup is broadly balanced for this model.52.8 tok/s 解码
~$1,999 MSRP
Can Yi 1.5 6B Chat run on RTX 2000 Ada 16GB?
YES — Runs Great
C50Usable
Estimated from fit model
Yi 1.5 6B Chat needs ~7.2 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~60 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) — 7.2 GB, 59.8 tok/s, Runs well
7.2 GB required16.0 GB available
45% VRAM used
Fit status
Runs well
Decode
59.8 tok/s
TTFT
3237 ms
Safe context
217K
Memory
7.2 GB / 16.0 GB
Memory breakdown
Weights3.7 GB
KV Cache0.7 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsYi 1.5 6B Chat 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: 59.8 tok/s decode · 3.2s TTFT (warm) · 150 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 | 59.8 tok/s | 1766 ms | 217K |
| Coding | C | Runs well | 59.8 tok/s | 3237 ms | 217K |
| Agentic Coding | C | Runs well | 59.8 tok/s | 4709 ms | 217K |
| Reasoning | C | Runs well | 59.8 tok/s | 3826 ms | 217K |
| RAG | C | Runs well | 59.8 tok/s | 5886 ms | 217K |
Quantization options
How Yi 1.5 6B Chat (6B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C46 |
Q3_K_S | 3 | 2.9 GB | Low | C47 |
NVFP4 | 4 | 3.4 GB | Medium | C47 |
Q4_K_M | 4 | 3.7 GB | Medium | C47 |
Q5_K_M | 5 | 4.3 GB | High | C48 |
Q6_K | 6 | 4.9 GB | High | C49 |
Q8_0 | 8 | 6.4 GB | Very High | C50 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Get started
Copy-paste commands to run Yi 1.5 6B Chat on your machine.
Run
lms load hf-bartowski--yi-1-5-6b-chat-gguf && lms server start升级选项
能流畅运行 Yi 1.5 6B Chat 的硬件
Frequently asked questions
Can RTX 2000 Ada 16GB run Yi 1.5 6B Chat?
Yes, RTX 2000 Ada 16GB can run Yi 1.5 6B Chat with a C grade (Runs well). Expected decode speed: 59.8 tok/s.
How much VRAM does Yi 1.5 6B Chat need?
Yi 1.5 6B Chat (6B parameters) requires approximately 7.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 1.5 6B Chat?
The recommended quantization for Yi 1.5 6B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 1.5 6B Chat run at on RTX 2000 Ada 16GB?
On RTX 2000 Ada 16GB, Yi 1.5 6B Chat achieves approximately 59.8 tokens per second decode speed with a time-to-first-token of 3237ms using Q4_K_M quantization.
Can RTX 2000 Ada 16GB run Yi 1.5 6B Chat for coding?
For coding workloads, Yi 1.5 6B Chat on RTX 2000 Ada 16GB receives a C grade with 59.8 tok/s and 217K context.
What context window can Yi 1.5 6B Chat use on RTX 2000 Ada 16GB?
On RTX 2000 Ada 16GB, Yi 1.5 6B Chat can safely use up to 217K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--yi-1-5-6b-chat-gguf-on-rtx-2000-ada-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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