Can Yi 34B Chat run on RTX 4070 12GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Yi 34B Chat needs ~26.5 GB but RTX 4070 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: StandardBottleneck: Memory capacity

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.5 GB, exceeds 12.0 GB available

26.5 GB required12.0 GB available

221% VRAM needed

14.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

26.5 GB / 12.0 GB

Offload

50%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsYi 34B Chat on RTX 4070 12GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 26.5 GB, but this setup only exposes 12.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.0 tok/s	35221 ms	4K
Coding	F	Too heavy	2.7 tok/s	70816 ms	4K
Agentic Coding	F	Too heavy	2.7 tok/s	103005 ms	4K
Reasoning	F	Too heavy	2.7 tok/s	83692 ms	4K
RAG	F	Too heavy	2.7 tok/s	128756 ms	4K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	F0
Q3_K_S	3	16.7 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Yi 34B Chat well

👁 NVIDIA
RTX 3090 24GBBest value

24 GB VRAM (+12)936 GB/s (+432)

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

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

Raises estimated decode speed by about 490%.

~$1,499 MSRP

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+12)672 GB/s (+168)

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

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

Raises estimated decode speed by about 745%.

~$1,599 MSRP

👁 NVIDIA
RTX 5090 32GBBudget pick

32 GB VRAM (+20)1792 GB/s (+1288)

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

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

See all results for RTX 4070 12GB See all hardware for Yi 34B Chat

URL: https://willitrunai.com/can-run/yi-34b-chat-on-rtx-4070-12gb

⇱

Can Yi 34B Chat run on RTX 4070 12GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Yi 34B Chat well

Frequently asked questions