Will It Run AI

Can Llama 3.3 70B Instruct run on Gaudi 3 128GB?

YES — Runs Great

C51Usable
Estimated from fit model

Llama 3.3 70B Instruct needs ~64.6 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~61 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 64.6 GB, 60.7 tok/s, Runs well
64.6 GB required128.0 GB available
50% VRAM used

Fit status

Runs well

Decode

60.7 tok/s

TTFT

3192 ms

Safe context

140K

Memory

64.6 GB / 128.0 GB

Memory breakdown

Weights42.7 GB
KV Cache8.2 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B Instruct 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: 60.7 tok/s decode · 3.2s TTFT (warm) · 152 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well60.7 tok/s1741 ms140K
CodingCRuns well60.7 tok/s3192 ms140K
Agentic CodingCRuns well60.7 tok/s4643 ms140K
ReasoningCRuns well60.7 tok/s3772 ms140K
RAGCRuns well60.7 tok/s5803 ms140K

Quantization options

How Llama 3.3 70B Instruct (70B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowC41
Q3_K_S
3
34.3 GB
LowC42
NVFP4
4
39.2 GB
MediumC43
Q4_K_M
4
42.7 GB
MediumC43
Q5_K_M
5
50.4 GB
HighC44
Q6_K
6
57.4 GB
HighC46
Q8_0Best for your GPU
8
74.9 GB
Very HighC48
F16
16
143.5 GB
MaximumF0

Get started

Copy-paste commands to run Llama 3.3 70B Instruct on your machine.

Run

lms load hf-maziyarpanahi--llama-3-3-70b-instruct-gguf && lms server start

Upgrade-Optionen

Hardware, die Llama 3.3 70B Instruct gut ausführt

NVIDIANVIDIA H200 141GBGünstige Wahl
141 GB VRAM (+13)4800 GB/s (+1100)
C
Raises estimated decode speed by about 56%.94.4 tok/s Dekodierung

Raises estimated decode speed by about 56%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

ca. $30,000 MSRP

NVIDIANVIDIA H200 PCIe 141GBBestes Preis-Leistungs-Verhältnis
141 GB VRAM (+13)4800 GB/s (+1100)
C
Raises estimated decode speed by about 56%.94.4 tok/s Dekodierung

Raises estimated decode speed by about 56%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

ca. $30,000 MSRP

Frequently asked questions

Can Gaudi 3 128GB run Llama 3.3 70B Instruct?

Yes, Gaudi 3 128GB can run Llama 3.3 70B Instruct with a C grade (Runs well). Expected decode speed: 60.7 tok/s.

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 64.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B Instruct?

The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B Instruct run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Llama 3.3 70B Instruct achieves approximately 60.7 tokens per second decode speed with a time-to-first-token of 3192ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Llama 3.3 70B Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on Gaudi 3 128GB receives a C grade with 60.7 tok/s and 140K context.

What context window can Llama 3.3 70B Instruct use on Gaudi 3 128GB?

On Gaudi 3 128GB, Llama 3.3 70B Instruct can safely use up to 140K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.3 70B Instruct 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 Llama 3.3 70B Instruct?

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 128GBSee all hardware for Llama 3.3 70B Instruct
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