Can Yi 34B Chat run on Gaudi 3 128GB?

YES — Runs Great

C50Usable

Estimated from fit model

Yi 34B Chat needs ~38.1 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~136 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) — 38.1 GB, 135.6 tok/s, Runs well

38.1 GB required128.0 GB available

30% VRAM used

Fit status

Runs well

Decode

135.6 tok/s

TTFT

1428 ms

Safe context

200K

Memory

38.1 GB / 128.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsYi 34B Chat on Gaudi 3 128GB
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: 135.6 tok/s decode · 1.4s TTFT (warm) · 339 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	135.6 tok/s	779 ms	200K
Coding	C	Runs well	135.6 tok/s	1428 ms	200K
Agentic Coding	C	Runs well	135.6 tok/s	2077 ms	200K
Reasoning	C	Runs well	135.6 tok/s	1688 ms	200K
RAG	C	Runs well	135.6 tok/s	2596 ms	200K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C40
Q3_K_S	3	16.7 GB	Low	C40
NVFP4	4	19.0 GB	Medium	C41
Q4_K_M	4	20.7 GB	Medium	C41
Q5_K_M	5	24.5 GB	High	C41
Q6_K	6	27.9 GB	High	C42
Q8_0	8	36.4 GB	Very High	C43
F16Best for your GPU	16	69.7 GB	Maximum	C49

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 Gaudi 3 128GB run Yi 34B Chat?

Yes, Gaudi 3 128GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 135.6 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 38.1 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 Gaudi 3 128GB?

On Gaudi 3 128GB, Yi 34B Chat achieves approximately 135.6 tokens per second decode speed with a time-to-first-token of 1428ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on Gaudi 3 128GB receives a C grade with 135.6 tok/s and 200K context.

What context window can Yi 34B Chat use on Gaudi 3 128GB?

On Gaudi 3 128GB, 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.

What should I upgrade first if Yi 34B Chat feels slow on Gaudi 3 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Gaudi 3 128GB for Yi 34B Chat?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Gaudi 3 128GB See all hardware for Yi 34B Chat

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