Can Llama 3.3 70B run on RTX 5000 Ada 32GB?

Q: What should I upgrade first if Llama 3.3 70B feels slow on RTX 5000 Ada 32GB?

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 Q2_K

A71Great

Estimated from fit model

Llama 3.3 70B needs ~36.6 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q2_K quantization, expect ~9 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: 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 52.0 GB — too much for RTX 5000 Ada 32GB (32.0 GB). Runs at Q2_K (36.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 52.0 GB, exceeds 32.0 GB available

52.0 GB required32.0 GB available

163% VRAM needed

20.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.2 tok/s

TTFT

61077 ms

Safe context

Memory

52.0 GB / 32.0 GB

Offload

40%

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3.3 70B on RTX 5000 Ada 32GB
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: 3.2 tok/s decode · 61.1s TTFT (warm) · 8 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	3.5 tok/s	30106 ms	4K
Coding	F	Too heavy	2.9 tok/s	66421 ms	4K
Agentic Coding	F	Too heavy	2.6 tok/s	107319 ms	4K
Reasoning	F	Too heavy	3.2 tok/s	72181 ms	4K
RAG	F	Too heavy	2.6 tok/s	134148 ms	4K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	F0
Q3_K_S	3	34.3 GB	Low	F0
NVFP4	4

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 (+16)768 GB/s (+192)

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.

Raises estimated decode speed by about 175%.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBNVIDIA upgrade

48 GB VRAM (+16)1344 GB/s (+768)

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 450%.

~$4,999 MSRP

NVIDIA A16 64GBBudget pick

64 GB VRAM (+32)600 GB/s (+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 (+48)3350 GB/s (+2774)

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 RTX 5000 Ada 32GB run Llama 3.3 70B?

Yes, RTX 5000 Ada 32GB can run Llama 3.3 70B at Q2_K quantization (Very compromised (needs ~3.4 GB host RAM)). The recommended Q4_K_M requires 52.0 GB which exceeds available memory, but at Q2_K it needs only 36.6 GB. Expected decode speed: 8.8 tok/s.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 52.0 GB at Q4_K_M quantization. On RTX 5000 Ada 32GB, it fits at Q2_K using 36.6 GB.

What is the best quantization for Llama 3.3 70B?

The recommended quantization is Q4_K_M, but on RTX 5000 Ada 32GB the best fitting quantization is Q2_K, which uses 36.6 GB.

What speed will Llama 3.3 70B run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Llama 3.3 70B achieves approximately 8.8 tokens per second decode speed with a time-to-first-token of 21928ms using Q2_K quantization.

Can RTX 5000 Ada 32GB run Llama 3.3 70B for coding?

For coding workloads, Llama 3.3 70B on RTX 5000 Ada 32GB receives a F grade with 2.9 tok/s and 4K context.

What context window can Llama 3.3 70B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Llama 3.3 70B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for Llama 3.3 70B

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