Can Yi 34B Chat run on RTX 5000 Ada 32GB?

YES — Tight Fit

C51Usable

Estimated from fit model

Yi 34B Chat needs ~28.5 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 28.5 GB, 24.1 tok/s, Tight fit

28.5 GB required32.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

24.1 tok/s

TTFT

8026 ms

Safe context

31K

Memory

28.5 GB / 32.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsYi 34B Chat on RTX 5000 Ada 32GB
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: 24.1 tok/s decode · 8.0s TTFT (warm) · 60 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	Tight fit	24.1 tok/s	4378 ms	31K
Coding	C	Tight fit	24.1 tok/s	8026 ms	31K
Agentic Coding	C	Runs with offload (needs ~0.1 GB host RAM)	17.9 tok/s	15734 ms	31K
Reasoning	C	Tight fit	24.1 tok/s	9485 ms	31K
RAG	C	Runs with offload (needs ~0.1 GB host RAM)	17.9 tok/s	19667 ms

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C49
Q3_K_S	3	16.7 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run Yi 34B Chat on your machine.

Run

lms load Yi-34B-Chat && lms server start

Upgrade options

Hardware that runs Yi 34B Chat well

RTX A6000 48GBBudget pick

48 GB VRAM (+16)768 GB/s (+192)

Raises estimated decode speed by about 27%.30.6 tok/s decode

Raises estimated decode speed by about 27%.

Adds memory headroom for longer context windows and future model growth.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+768)

Raises estimated decode speed by about 145%.59.1 tok/s decode

Raises estimated decode speed by about 145%.

Adds memory headroom for longer context windows and future model growth.

~$4,999 MSRP

NVIDIA A40 48GBNVIDIA upgrade

48 GB VRAM (+16)696 GB/s (+120)

Adds memory headroom for longer context windows and future model growth.28.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$5,500 MSRP

Frequently asked questions

Can RTX 5000 Ada 32GB run Yi 34B Chat?

Yes, RTX 5000 Ada 32GB can run Yi 34B Chat with a C grade (Tight fit). Expected decode speed: 24.1 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 28.5 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 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Yi 34B Chat achieves approximately 24.1 tokens per second decode speed with a time-to-first-token of 8026ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on RTX 5000 Ada 32GB receives a C grade with 24.1 tok/s and 31K context.

What context window can Yi 34B Chat use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Yi 34B Chat can safely use up to 31K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for Yi 34B Chat

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