Can Yi 34B Chat run on RTX 6000 Ada 48GB?

YES — Runs Great

C54Usable

Estimated from fit model

Yi 34B Chat needs ~30.1 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~38 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 30.1 GB, 41.2 tok/s, Runs well

30.1 GB required48.0 GB available

63% VRAM used

Fit status

Runs well

Decode

41.2 tok/s

TTFT

4698 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 RTX 6000 Ada 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: 41.2 tok/s decode · 4.7s TTFT (warm) · 103 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	41.2 tok/s	2563 ms	94K
Coding	C	Runs well	38.0 tok/s	5101 ms	94K
Agentic Coding	B	Runs well	41.2 tok/s	6833 ms	94K
Reasoning	C	Runs well	41.2 tok/s	5552 ms	94K
RAG	B	Runs well	41.2 tok/s	8542 ms	94K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on RTX 6000 Ada 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

Frequently asked questions

Can RTX 6000 Ada 48GB run Yi 34B Chat?

Yes, RTX 6000 Ada 48GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 38.0 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 RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Yi 34B Chat achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5101ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on RTX 6000 Ada 48GB receives a C grade with 38.0 tok/s and 94K context.

What context window can Yi 34B Chat use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 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.

See all results for RTX 6000 Ada 48GB See all hardware for Yi 34B Chat

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