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

Q: What should I upgrade first if Llama 3.3 70B 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

A74Great

Estimated from fit model

Llama 3.3 70B needs ~44.4 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q3_K_S quantization, expect ~23 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 at Q4_K_M needs 52.8 GB — too much for NVIDIA A100 40GB (40.0 GB). Runs at Q3_K_S (44.4 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 52.8 GB, exceeds 40.0 GB available

52.8 GB required40.0 GB available

132% VRAM needed

12.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.9 tok/s

TTFT

13911 ms

Safe context

Memory

52.8 GB / 40.0 GB

Offload

20%

Memory breakdown

Weights42.7 GB

KV Cache4.9 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 on NVIDIA A100 40GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 13.9 tok/s decode · 13.9s TTFT (warm) · 35 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 10% 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 3.4 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	15.4 tok/s	6868 ms	4K
Coding	F	Too heavy	13.9 tok/s	13911 ms	4K
Agentic Coding	F	Too heavy	11.6 tok/s	24376 ms	4K
Reasoning	F	Too heavy	13.9 tok/s	16440 ms	4K
RAG	F	Too heavy	11.6 tok/s	30469 ms	4K

Inference speed

Llama 3.3 70B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3.3 70B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~18 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	18.0	Heavy offload

Quantization options

How Llama 3.3 70B (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	A82
Q3_K_S	3	34.3 GB	Low	F0

Get started

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

Run

ollama run llama3.3

Upgrade options

Hardware that runs Llama 3.3 70B well

RTX A6000 48GBBest value

48 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.8.8 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.17.6 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.11.9 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.

~$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.71.7 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?

Yes, NVIDIA A100 40GB can run Llama 3.3 70B at Q3_K_S quantization (Very compromised (needs ~3.4 GB host RAM)). The recommended Q4_K_M requires 52.8 GB which exceeds available memory, but at Q3_K_S it needs only 44.4 GB. Expected decode speed: 23.2 tok/s.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 52.8 GB at Q4_K_M quantization. On NVIDIA A100 40GB, it fits at Q3_K_S using 44.4 GB.

What is the best quantization for Llama 3.3 70B?

The recommended quantization is Q4_K_M, but on NVIDIA A100 40GB the best fitting quantization is Q3_K_S, which uses 44.4 GB.

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

On NVIDIA A100 40GB, Llama 3.3 70B achieves approximately 23.2 tokens per second decode speed with a time-to-first-token of 8342ms using Q3_K_S quantization.

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

For coding workloads, Llama 3.3 70B on NVIDIA A100 40GB receives a F grade with 13.9 tok/s and 4K context.

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

On NVIDIA A100 40GB, Llama 3.3 70B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

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

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