NVIDIA A100 80GB经济之选80 GB VRAM (+32)2039 GB/s (+1175)
C
Raises estimated decode speed by about 372%.89.7 tok/s 解码
Raises estimated decode speed by about 372%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
Can Yi 34B Chat run on NVIDIA L20 48GB?
YES — Runs Great
C52Usable
Estimated from fit model
Yi 34B Chat needs ~30.1 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~19 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 30.1 GB, 19.0 tok/s, Runs well
30.1 GB required48.0 GB available
63% VRAM used
Fit status
Runs well
Decode
19.0 tok/s
TTFT
10180 ms
Safe context
94K
Memory
30.1 GB / 48.0 GB
Memory breakdown
Weights20.7 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsYi 34B Chat on NVIDIA L20 48GB
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: 19.0 tok/s decode · 10.2s TTFT (warm) · 48 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 | 19.0 tok/s | 5553 ms | 94K |
| Coding | C | Runs well | 19.0 tok/s | 10180 ms | 94K |
| Agentic Coding | C | Runs well | 19.0 tok/s | 14807 ms | 94K |
| Reasoning | C | Runs well | 19.0 tok/s | 12031 ms | 94K |
| RAG | C | Runs well | 19.0 tok/s | 18509 ms | 94K |
Quantization options
How Yi 34B Chat (34B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | C46 |
Q3_K_S | 3 | 16.7 GB | Low | C47 |
NVFP4 | 4 | 19.0 GB | Medium | C47 |
Q4_K_M | 4 | 20.7 GB | Medium | C48 |
Q5_K_M | 5 | 24.5 GB | High | C49 |
Q6_K | 6 | 27.9 GB | High | C50 |
Q8_0Best for your GPU | 8 | 36.4 GB | Very High | C49 |
F16 | 16 | 69.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi 34B Chat on your machine.
Run
lms load Yi-34B-Chat && lms server start升级选项
能流畅运行 Yi 34B Chat 的硬件
NVIDIA A100 80GB经济之选NVIDIA A800 80GB最佳性价比80 GB VRAM (+32)1935 GB/s (+1071)
C
Raises estimated decode speed by about 316%.79 tok/s 解码
Raises estimated decode speed by about 316%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
NVIDIA H800 80GBNVIDIA 升级80 GB VRAM (+32)3000 GB/s (+2136)
C
Raises estimated decode speed by about 569%.127.2 tok/s 解码
Raises estimated decode speed by about 569%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Frequently asked questions
Can NVIDIA L20 48GB run Yi 34B Chat?
Yes, NVIDIA L20 48GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 19.0 tok/s.
How much VRAM does Yi 34B Chat need?
Yi 34B Chat (34B parameters) requires approximately 30.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 34B Chat?
The recommended quantization for Yi 34B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 34B Chat run at on NVIDIA L20 48GB?
On NVIDIA L20 48GB, Yi 34B Chat achieves approximately 19.0 tokens per second decode speed with a time-to-first-token of 10180ms using Q4_K_M quantization.
Can NVIDIA L20 48GB run Yi 34B Chat for coding?
For coding workloads, Yi 34B Chat on NVIDIA L20 48GB receives a C grade with 19.0 tok/s and 94K context.
What context window can Yi 34B Chat use on NVIDIA L20 48GB?
On NVIDIA L20 48GB, Yi 34B Chat can safely use up to 94K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.
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