Can Llama 3.3 70B Instruct run on NVIDIA A100 40GB?

Q: What should I upgrade first if Llama 3.3 70B Instruct feels slow on NVIDIA A100 40GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

D33Poor

Estimated from fit model

Llama 3.3 70B Instruct needs ~47.7 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q3_K_S quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload

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.

Llama 3.3 70B Instruct at Q4_K_M needs 56.1 GB — too much for NVIDIA A100 40GB (40.0 GB). Runs at Q3_K_S (47.7 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 56.1 GB, exceeds 40.0 GB available

56.1 GB required40.0 GB available

140% VRAM needed

16.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

11.3 tok/s

TTFT

17201 ms

Safe context

Memory

56.1 GB / 40.0 GB

Offload

30%

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3.3 70B Instruct on NVIDIA A100 40GB
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: 11.3 tok/s decode · 17.2s TTFT (warm) · 28 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 5.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	13.2 tok/s	7996 ms	4K
Coding	F	Too heavy	11.3 tok/s	17201 ms	4K
Agentic Coding	F	Too heavy	8.4 tok/s	33345 ms	4K
Reasoning	F	Too heavy	11.3 tok/s	20328 ms	4K
RAG	F	Too heavy	8.4 tok/s	41681 ms	4K

Quantization options

How Llama 3.3 70B Instruct (70B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	27.3 GB	Low	C48
Q3_K_S	3	34.3 GB	Low	F0

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 options

Hardware that runs Llama 3.3 70B Instruct well

RTX A6000 48GBBest value

48 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.7.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBNVIDIA upgrade

48 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.14.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 27%.

~$4,999 MSRP

NVIDIA A16 64GBBudget pick

64 GB VRAM (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.11 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$6,500 MSRP

NVIDIA H100 80GBBiggest leap

80 GB VRAM (+40)3350 GB/s (+1795)

Makes the model fit on the accelerator instead of staying completely out of reach.65.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$40,000 MSRP

Frequently asked questions

Can NVIDIA A100 40GB run Llama 3.3 70B Instruct?

Yes, NVIDIA A100 40GB can run Llama 3.3 70B Instruct at Q3_K_S quantization (Very compromised (needs ~5.5 GB host RAM)). The recommended Q4_K_M requires 56.1 GB which exceeds available memory, but at Q3_K_S it needs only 47.7 GB. Expected decode speed: 18.3 tok/s.

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 56.1 GB at Q4_K_M quantization. On NVIDIA A100 40GB, it fits at Q3_K_S using 47.7 GB.

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

The recommended quantization is Q4_K_M, but on NVIDIA A100 40GB the best fitting quantization is Q3_K_S, which uses 47.7 GB.

What speed will Llama 3.3 70B Instruct run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Llama 3.3 70B Instruct achieves approximately 18.3 tokens per second decode speed with a time-to-first-token of 10560ms using Q3_K_S quantization.

Can NVIDIA A100 40GB run Llama 3.3 70B Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on NVIDIA A100 40GB receives a F grade with 11.3 tok/s and 4K context.

What context window can Llama 3.3 70B Instruct use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Llama 3.3 70B Instruct can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Llama 3.3 70B Instruct

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