Can Qwen 3 1.7B run on Gaudi 3 128GB?

YES — Runs Great

B60Good

Estimated from fit model

Qwen 3 1.7B needs ~16.4 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~24 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) — 16.4 GB, 23.8 tok/s, Runs well

16.4 GB required128.0 GB available

13% VRAM used

Fit status

Runs well

Decode

23.8 tok/s

TTFT

8134 ms

Safe context

33K

Memory

16.4 GB / 128.0 GB

Memory breakdown

Weights1.0 GB

KV Cache1.7 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsQwen 3 1.7B 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: 23.8 tok/s decode · 8.1s TTFT (warm) · 60 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	B	Runs well	23.8 tok/s	4437 ms	33K
Coding	B	Runs well	23.8 tok/s	8134 ms	33K
Agentic Coding	B	Runs well	23.8 tok/s	11832 ms	33K
Reasoning	B	Runs well	23.8 tok/s	9613 ms	33K
RAG	B	Runs well	23.8 tok/s	14790 ms	33K

Quantization options

How Qwen 3 1.7B (1.7000000476837158B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.7 GB	Low	B58
Q3_K_S	3	0.8 GB	Low	B58
NVFP4	4	1.0 GB	Medium	B58
Q4_K_M	4	1.0 GB	Medium	B58
Q5_K_M	5	1.2 GB	High	B58
Q6_K	6	1.4 GB	High	B58
Q8_0	8	1.8 GB	Very High	B58
F16Best for your GPU	16	3.5 GB	Maximum	B58

Get started

Copy-paste commands to run Qwen 3 1.7B on your machine.

Run

ollama run qwen3:1.7b

Opciones de mejora

Hardware que ejecuta bien Qwen 3 1.7B

Mac Studio M3 Ultra 256GBOpción económica

256 GB Unified (+128)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.23.8 tok/s decodificación

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

~$6,999 MSRP

Frequently asked questions

Can Gaudi 3 128GB run Qwen 3 1.7B?

Yes, Gaudi 3 128GB can run Qwen 3 1.7B with a B grade (Runs well). Expected decode speed: 23.8 tok/s.

How much VRAM does Qwen 3 1.7B need?

Qwen 3 1.7B (1.7000000476837158B parameters) requires approximately 16.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 1.7B?

The recommended quantization for Qwen 3 1.7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3 1.7B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Qwen 3 1.7B achieves approximately 23.8 tokens per second decode speed with a time-to-first-token of 8134ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Qwen 3 1.7B for coding?

For coding workloads, Qwen 3 1.7B on Gaudi 3 128GB receives a B grade with 23.8 tok/s and 33K context.

What context window can Qwen 3 1.7B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Qwen 3 1.7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3 1.7B 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 Qwen 3 1.7B?

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 Qwen 3 1.7B

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