Can Yi 34B Chat run on NVIDIA B200 180GB?

YES — Runs Great

C49Usable

Estimated from fit model

Yi 34B Chat needs ~43.3 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~352 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) — 43.3 GB, 351.8 tok/s, Runs well

43.3 GB required180.0 GB available

24% VRAM used

Fit status

Runs well

Decode

351.8 tok/s

TTFT

550 ms

Safe context

200K

Memory

43.3 GB / 180.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsYi 34B Chat on NVIDIA B200 180GB
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: 351.8 tok/s decode · 550ms TTFT (warm) · 880 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	351.8 tok/s	350 ms	200K
Coding	C	Runs well	351.8 tok/s	550 ms	200K
Agentic Coding	C	Runs well	351.8 tok/s	800 ms	200K
Reasoning	C	Runs well	351.8 tok/s	650 ms	200K
RAG	C	Runs well	351.8 tok/s	1001 ms	200K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	D39
Q3_K_S	3	16.7 GB	Low	D39
NVFP4	4	19.0 GB	Medium	D39
Q4_K_M	4	20.7 GB	Medium	D39
Q5_K_M	5	24.5 GB	High	D40
Q6_K	6	27.9 GB	High	C40
Q8_0	8	36.4 GB	Very High	C41
F16Best for your GPU	16	69.7 GB	Maximum	C45

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 NVIDIA B200 180GB run Yi 34B Chat?

Yes, NVIDIA B200 180GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 351.8 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 43.3 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 NVIDIA B200 180GB?

On NVIDIA B200 180GB, Yi 34B Chat achieves approximately 351.8 tokens per second decode speed with a time-to-first-token of 550ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on NVIDIA B200 180GB receives a C grade with 351.8 tok/s and 200K context.

What context window can Yi 34B Chat use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Yi 34B Chat can safely use up to 200K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for Yi 34B Chat

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

351.8 tok/s

350 ms

200K

Coding

Runs well

351.8 tok/s

550 ms

200K

Agentic Coding

Runs well

351.8 tok/s

800 ms

200K

Reasoning

Runs well

351.8 tok/s

650 ms

200K

RAG

Runs well

351.8 tok/s

1001 ms

200K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	D39
Q3_K_S	3	16.7 GB	Low	D39
NVFP4	4	19.0 GB	Medium	D39
Q4_K_M	4	20.7 GB	Medium	D39
Q5_K_M	5	24.5 GB	High	D40
Q6_K	6	27.9 GB	High	C40
Q8_0	8	36.4 GB	Very High	C41
F16Best for your GPU	16	69.7 GB	Maximum	C45

Frequently asked questions

Can NVIDIA B200 180GB run Yi 34B Chat?

Yes, NVIDIA B200 180GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 351.8 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 43.3 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 NVIDIA B200 180GB?

On NVIDIA B200 180GB, Yi 34B Chat achieves approximately 351.8 tokens per second decode speed with a time-to-first-token of 550ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on NVIDIA B200 180GB receives a C grade with 351.8 tok/s and 200K context.

What context window can Yi 34B Chat use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Yi 34B Chat can safely use up to 200K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.