Would CUDA be a better path than Gaudi 3 128GB for TinyLlama 1.1B Chat v1.0?

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.

Can TinyLlama 1.1B Chat v1.0 run on Gaudi 3 128GB?

Q: What should I upgrade first if TinyLlama 1.1B Chat v1.0 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.

YES — Runs Great

D39Poor

Estimated from fit model

TinyLlama 1.1B Chat v1.0 needs ~14.5 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~15 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) — 14.5 GB, 15.4 tok/s, Runs well

14.5 GB required128.0 GB available

11% VRAM used

Fit status

Runs well

Decode

15.4 tok/s

TTFT

12571 ms

Safe context

14.1M

Memory

14.5 GB / 128.0 GB

Memory breakdown

Weights0.7 GB

KV Cache0.1 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsTinyLlama 1.1B Chat v1.0 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: 15.4 tok/s decode · 12.6s TTFT (warm) · 39 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	D	Runs well	15.4 tok/s	6857 ms	9.1M
Coding	D	Runs well	15.4 tok/s	12571 ms	14.1M
Agentic Coding	D	Runs well	15.4 tok/s	18286 ms	14.1M
Reasoning	D	Runs well	15.4 tok/s	14857 ms	14.1M
RAG	D	Runs well	15.4 tok/s	22857 ms	14.1M

Quantization options

How TinyLlama 1.1B Chat v1.0 (1.100000023841858B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.4 GB	Low	D38
Q3_K_S	3	0.5 GB	Low	D38
NVFP4	4

Get started

Copy-paste commands to run TinyLlama 1.1B Chat v1.0 on your machine.

Run

lms load hf-thebloke--tinyllama-1-1b-chat-v1-0-gguf && lms server start

Upgrade options

Hardware that runs TinyLlama 1.1B Chat v1.0 well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)

Adds memory headroom for longer context windows and future model growth.15.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$6,999 MSRP

Frequently asked questions

Can Gaudi 3 128GB run TinyLlama 1.1B Chat v1.0?

Yes, Gaudi 3 128GB can run TinyLlama 1.1B Chat v1.0 with a D grade (Runs well). Expected decode speed: 15.4 tok/s.

How much VRAM does TinyLlama 1.1B Chat v1.0 need?

TinyLlama 1.1B Chat v1.0 (1.100000023841858B parameters) requires approximately 14.5 GB of memory with Q4_K_M quantization.

What is the best quantization for TinyLlama 1.1B Chat v1.0?

The recommended quantization for TinyLlama 1.1B Chat v1.0 is Q4_K_M, which balances quality and memory efficiency.

What speed will TinyLlama 1.1B Chat v1.0 run at on Gaudi 3 128GB?

On Gaudi 3 128GB, TinyLlama 1.1B Chat v1.0 achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.

Can Gaudi 3 128GB run TinyLlama 1.1B Chat v1.0 for coding?

For coding workloads, TinyLlama 1.1B Chat v1.0 on Gaudi 3 128GB receives a D grade with 15.4 tok/s and 14.1M context.

What context window can TinyLlama 1.1B Chat v1.0 use on Gaudi 3 128GB?

On Gaudi 3 128GB, TinyLlama 1.1B Chat v1.0 can safely use up to 14.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Gaudi 3 128GB See all hardware for TinyLlama 1.1B Chat v1.0

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