Can Llama 3.1 70B run on RTX PRO 5000 Blackwell 48GB?

Q: What should I upgrade first if Llama 3.1 70B feels slow on RTX PRO 5000 Blackwell 48GB?

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.

BARELY — Tight on Memory

B70Good

Estimated from fit model

Llama 3.1 70B needs ~53.3 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 53.3 GB, 17.8 tok/s, Very compromised (needs ~4.2 GB host RAM)

53.3 GB required48.0 GB available

111% VRAM needed

5.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~4.2 GB host RAM)

Decode

17.8 tok/s

TTFT

10895 ms

Safe context

Memory

53.3 GB / 48.0 GB

Offload

10%

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsLlama 3.1 70B on RTX PRO 5000 Blackwell 48GB
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: 17.8 tok/s decode · 10.9s TTFT (warm) · 44 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 4.2 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	A	Runs with offload (needs ~2.4 GB host RAM)	19.6 tok/s	5401 ms	4K
Coding	B	Very compromised (needs ~4.2 GB host RAM)	17.8 tok/s	10895 ms	4K
Agentic Coding	F	Too heavy	14.9 tok/s	18952 ms	4K
Reasoning	B	Very compromised	16.3 tok/s	14003 ms	4K
RAG	F	Too heavy	14.9 tok/s	23689 ms

Quantization options

How Llama 3.1 70B (70B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A79
Q3_K_SBest for your GPU	3	34.3 GB	Low	A79

Get started

Copy-paste commands to run Llama 3.1 70B on your machine.

Run

ollama run llama3.1

Upgrade options

Hardware that runs Llama 3.1 70B well

NVIDIA A16 64GBBudget pick

64 GB VRAM (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.11.9 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$6,500 MSRP

RTX PRO 6000 Blackwell Workstation Edition 96GBBest value

96 GB VRAM (+48)1792 GB/s (+448)

Removes host-memory offload, which is usually the single biggest latency and throughput win.38.3 tok/s decode

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

Raises estimated decode speed by about 115%.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBNVIDIA upgrade

96 GB VRAM (+48)1597 GB/s (+253)

Removes host-memory offload, which is usually the single biggest latency and throughput win.34.2 tok/s decode

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

Raises estimated decode speed by about 92%.

~$9,999 MSRP

Frequently asked questions

Can RTX PRO 5000 Blackwell 48GB run Llama 3.1 70B?

Yes, RTX PRO 5000 Blackwell 48GB can run Llama 3.1 70B with a B grade (Very compromised (needs ~4.2 GB host RAM)). Expected decode speed: 17.8 tok/s.

How much VRAM does Llama 3.1 70B need?

Llama 3.1 70B (70B parameters) requires approximately 53.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 70B?

The recommended quantization for Llama 3.1 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 70B run at on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Llama 3.1 70B achieves approximately 17.8 tokens per second decode speed with a time-to-first-token of 10895ms using Q4_K_M quantization.

Can RTX PRO 5000 Blackwell 48GB run Llama 3.1 70B for coding?

For coding workloads, Llama 3.1 70B on RTX PRO 5000 Blackwell 48GB receives a B grade with 17.8 tok/s and 4K context.

What context window can Llama 3.1 70B use on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Llama 3.1 70B can safely use up to 4K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for Llama 3.1 70B

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