Can Yi 1.5 6B run on NVIDIA H100 80GB?

YES — Runs Great

C45Usable

Estimated from fit model

Yi 1.5 6B needs ~13.8 GB VRAM. NVIDIA H100 80GB has 80.0 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) — 13.8 GB, 84.0 tok/s, Runs well

13.8 GB required80.0 GB available

17% VRAM used

Fit status

Runs well

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

Memory

13.8 GB / 80.0 GB

Memory breakdown

Weights3.7 GB

KV Cache1.0 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsYi 1.5 6B on NVIDIA H100 80GB
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 H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	D39
Q3_K_S	3	2.9 GB	Low	D39
NVFP4	4	3.4 GB	Medium	D39
Q4_K_M	4	3.7 GB	Medium	D39
Q5_K_M	5	4.3 GB	High	D39
Q6_K	6	4.9 GB	High	D39
Q8_0	8	6.4 GB	Very High	D40
F16Best for your GPU	16	12.3 GB	Maximum	C40

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

Upgrade options

Hardware that runs Yi 1.5 6B well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.71.3 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.84 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA H100 80GB run Yi 1.5 6B?

Yes, NVIDIA H100 80GB 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 13.8 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 H100 80GB?

On NVIDIA H100 80GB, 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 H100 80GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on NVIDIA H100 80GB receives a C grade with 84.0 tok/s and 4K context.

What context window can Yi 1.5 6B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, 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 H100 80GB See all hardware for Yi 1.5 6B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

84.0 tok/s

1257 ms

Coding

Runs well

84.0 tok/s

2305 ms

Agentic Coding

Runs well

84.0 tok/s

3352 ms

Reasoning

Runs well

84.0 tok/s

2724 ms

RAG

Runs well

84.0 tok/s

4190 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.3 GB

Low

D39

Q3_K_S

2.9 GB

Low

D39

NVFP4

3.4 GB

Medium

D39

Q4_K_M

3.7 GB

Medium

D39

Q5_K_M

4.3 GB

High

D39

Q6_K

4.9 GB

High

D39

Q8_0

6.4 GB

Very High

D40

F16Best for your GPU

12.3 GB

Maximum

C40

Frequently asked questions

Can NVIDIA H100 80GB run Yi 1.5 6B?

Yes, NVIDIA H100 80GB 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 13.8 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 H100 80GB?

On NVIDIA H100 80GB, 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 H100 80GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on NVIDIA H100 80GB receives a C grade with 84.0 tok/s and 4K context.

What context window can Yi 1.5 6B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, 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.