Can Yi 34B Chat run on RTX 4000 Ada 20GB?

YES — With Q3_K_S

D38Poor

Estimated from fit model

Yi 34B Chat needs ~23.2 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q3_K_S quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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.

Yi 34B Chat at Q4_K_M needs 27.3 GB — too much for RTX 4000 Ada 20GB (20.0 GB). Runs at Q3_K_S (23.2 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 27.3 GB, exceeds 20.0 GB available

27.3 GB required20.0 GB available

137% VRAM needed

7.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.7 tok/s

TTFT

33812 ms

Safe context

Memory

27.3 GB / 20.0 GB

Offload

30%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsYi 34B Chat 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: 5.7 tok/s decode · 33.8s TTFT (warm) · 14 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.6 tok/s	15935 ms	4K
Coding	F	Too heavy	5.7 tok/s	33812 ms	4K
Agentic Coding	F	Too heavy	4.4 tok/s	64100 ms	4K
Reasoning	F	Too heavy	5.7 tok/s	39959 ms	4K
RAG	F	Too heavy	4.4 tok/s	80125 ms	4K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	13.3 GB	Low	C52
Q3_K_S	3	16.7 GB	Low	F0
NVFP4	4	19.0 GB	Medium	F0
Q4_K_M	4	20.7 GB	Medium	F0
Q5_K_M	5	24.5 GB	High	F0
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
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

RTX 3090 24GB最佳性价比

24 GB VRAM (+4)936 GB/s (+576)

Makes the model fit on the accelerator instead of staying completely out of reach.11.8 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 107%.

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA 升级

24 GB VRAM (+4)672 GB/s (+312)

Makes the model fit on the accelerator instead of staying completely out of reach.16.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 196%.

~$1,599 MSRP

RTX 5090 32GB经济之选

32 GB VRAM (+12)1792 GB/s (+1432)

Makes the model fit on the accelerator instead of staying completely out of reach.40.7 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run Yi 34B Chat?

Yes, RTX 4000 Ada 20GB can run Yi 34B Chat at Q3_K_S quantization (Very compromised (needs ~2.3 GB host RAM)). The recommended Q4_K_M requires 27.3 GB which exceeds available memory, but at Q3_K_S it needs only 23.2 GB. Expected decode speed: 9.3 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 27.3 GB at Q4_K_M quantization. On RTX 4000 Ada 20GB, it fits at Q3_K_S using 23.2 GB.

What is the best quantization for Yi 34B Chat?

The recommended quantization is Q4_K_M, but on RTX 4000 Ada 20GB the best fitting quantization is Q3_K_S, which uses 23.2 GB.

What speed will Yi 34B Chat run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Yi 34B Chat achieves approximately 9.3 tokens per second decode speed with a time-to-first-token of 20772ms using Q3_K_S quantization.

Can RTX 4000 Ada 20GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on RTX 4000 Ada 20GB receives a F grade with 5.7 tok/s and 4K context.

What context window can Yi 34B Chat use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Yi 34B Chat can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 200K, but available memory constrains the safe maximum.

What should I upgrade first if Yi 34B Chat feels slow on RTX 4000 Ada 20GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 4000 Ada 20GB See all hardware for Yi 34B Chat

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