Can Yi 34B Chat run on RTX 2070 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Yi 34B Chat needs ~26.1 GB but RTX 2070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: 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) — 26.1 GB, exceeds 8.0 GB available

26.1 GB required8.0 GB available

326% VRAM needed

18.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.1 tok/s

TTFT

91723 ms

Safe context

Memory

26.1 GB / 8.0 GB

Offload

70%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsYi 34B Chat on RTX 2070 8GB
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: 2.1 tok/s decode · 91.7s 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 26.1 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

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	2.1 tok/s	50031 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.1 tok/s	133415 ms	4K
Reasoning	F	Too heavy	2.1 tok/s	108400 ms	4K
RAG	F	Too heavy	2.1 tok/s	166769 ms	4K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on RTX 2070 8GB (8.0 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

RTX 3090 24GBBest value

24 GB VRAM (+16)936 GB/s (+488)

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 462%.

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+16)672 GB/s (+224)

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 705%.

~$1,599 MSRP

RTX 5090 32GBBudget pick

32 GB VRAM (+24)1792 GB/s (+1344)

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

Can RTX 2070 8GB run Yi 34B Chat?

No, Yi 34B Chat requires more memory than RTX 2070 8GB provides.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 26.1 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 RTX 2070 8GB?

On RTX 2070 8GB, Yi 34B Chat achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX 2070 8GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on RTX 2070 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Yi 34B Chat use on RTX 2070 8GB?

On RTX 2070 8GB, Yi 34B Chat can safely use up to 4K tokens of context. 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 2070 8GB?

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.

See all results for RTX 2070 8GB See all hardware for Yi 34B Chat

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/yi-34b-chat-on-rtx-2070-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: