Can Yi 1.5 34B run on RTX 6000 Ada 48GB?

YES — Runs Great

B65Good

Estimated from fit model

Yi 1.5 34B needs ~30.4 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 30.4 GB, 41.2 tok/s, Runs well

30.4 GB required48.0 GB available

63% VRAM used

Fit status

Runs well

Decode

41.2 tok/s

TTFT

4698 ms

Safe context

Memory

30.4 GB / 48.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsYi 1.5 34B on RTX 6000 Ada 48GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
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	B	Runs well	41.2 tok/s	2563 ms	4K
Coding	B	Runs well	38.0 tok/s	5101 ms	4K
Agentic Coding	B	Runs well	41.2 tok/s	6833 ms	4K
Reasoning	B	Runs well	41.2 tok/s	5552 ms	4K
RAG	B	Runs well	41.2 tok/s	8542 ms	4K

Quantization options

How Yi 1.5 34B (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	B57
Q3_K_S	3	16.7 GB	Low	B58
NVFP4	4

Get started

Copy-paste commands to run Yi 1.5 34B on your machine.

Run

lms load Yi-1.5-34B-Chat && lms server start

Frequently asked questions

Can RTX 6000 Ada 48GB run Yi 1.5 34B?

Yes, RTX 6000 Ada 48GB can run Yi 1.5 34B with a B grade (Runs well). Expected decode speed: 38.0 tok/s.

How much VRAM does Yi 1.5 34B need?

Yi 1.5 34B (34B parameters) requires approximately 30.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 34B?

The recommended quantization for Yi 1.5 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 34B run at on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Yi 1.5 34B 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 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on RTX 6000 Ada 48GB receives a B grade with 38.0 tok/s and 4K context.

What context window can Yi 1.5 34B use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Yi 1.5 34B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada 48GB See all hardware for Yi 1.5 34B

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