Can Yi 1.5 6B run on NVIDIA H200 141GB?

YES — Runs Great

C44Usable

Estimated from fit model

Yi 1.5 6B needs ~19.9 GB VRAM. NVIDIA H200 141GB has 141.0 GB. With Q4_K_M quantization, expect ~84 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) — 19.9 GB, 84.0 tok/s, Runs well

19.9 GB required141.0 GB available

14% VRAM used

Fit status

Runs well

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

Memory

19.9 GB / 141.0 GB

Memory breakdown

Weights3.7 GB

KV Cache1.0 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsYi 1.5 6B on NVIDIA H200 141GB
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: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 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	84.0 tok/s	1257 ms	4K
Coding	C	Runs well	84.0 tok/s	2305 ms	4K
Agentic Coding	C	Runs well	84.0 tok/s	3352 ms	4K
Reasoning	C	Runs well	84.0 tok/s	2724 ms	4K
RAG	C	Runs well	84.0 tok/s	4190 ms	4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on NVIDIA H200 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	D38
Q3_K_S	3	2.9 GB	Low	D38
NVFP4	4	3.4 GB	Medium	D38
Q4_K_M	4	3.7 GB	Medium	D38
Q5_K_M	5	4.3 GB	High	D38
Q6_K	6	4.9 GB	High	D38
Q8_0	8	6.4 GB	Very High	D38
F16Best for your GPU	16	12.3 GB	Maximum	D38

Get started

Copy-paste commands to run Yi 1.5 6B on your machine.

Run

lms load Yi-1.5-6B-Chat && lms server start

Opciones de mejora

Hardware que ejecuta bien Yi 1.5 6B

Mac Studio M3 Ultra 256GBOpción económica

256 GB Unified (+115)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.84 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$6,999 MSRP

Frequently asked questions

Can NVIDIA H200 141GB run Yi 1.5 6B?

Yes, NVIDIA H200 141GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 84.0 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 19.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 6B?

The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 6B run at on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Yi 1.5 6B achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.

Can NVIDIA H200 141GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on NVIDIA H200 141GB receives a C grade with 84.0 tok/s and 4K context.

What context window can Yi 1.5 6B use on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Yi 1.5 6B 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 NVIDIA H200 141GB See all hardware for Yi 1.5 6B

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