Can Llama 3.3 70B Instruct run on RTX PRO 5000 Blackwell 48GB?

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

D32Poor

Estimated from fit model

Llama 3.3 70B Instruct needs ~56.6 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~14 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) — 56.6 GB, 14.4 tok/s, Very compromised (needs ~6.5 GB host RAM)

56.6 GB required48.0 GB available

118% VRAM needed

8.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~6.5 GB host RAM)

Decode

14.4 tok/s

TTFT

13404 ms

Safe context

Memory

56.6 GB / 48.0 GB

Offload

20%

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B Instruct on RTX PRO 5000 Blackwell 48GB
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: 14.4 tok/s decode · 13.4s TTFT (warm) · 36 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 6.5 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	D	Very compromised	16.8 tok/s	6271 ms	4K
Coding	D	Very compromised (needs ~6.5 GB host RAM)	14.4 tok/s	13404 ms	4K
Agentic Coding	F	Too heavy	11.0 tok/s	25695 ms	4K
Reasoning	D	Very compromised (needs ~6.5 GB host RAM)	14.4 tok/s	15841 ms	4K
RAG	F	Too heavy	11.0 tok/s	32119 ms

Quantization options

How Llama 3.3 70B Instruct (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	C48
Q3_K_SBest for your GPU	3	34.3 GB	Low	C48

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

NVIDIA A16 64GBBudget pick

64 GB VRAM (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.11 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.35.3 tok/s decode

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

Raises estimated decode speed by about 145%.

~$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.31.4 tok/s decode

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

Raises estimated decode speed by about 118%.

~$9,999 MSRP

Frequently asked questions

Can RTX PRO 5000 Blackwell 48GB run Llama 3.3 70B Instruct?

Yes, RTX PRO 5000 Blackwell 48GB can run Llama 3.3 70B Instruct with a D grade (Very compromised (needs ~6.5 GB host RAM)). Expected decode speed: 14.4 tok/s.

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 56.6 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 RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Llama 3.3 70B Instruct achieves approximately 14.4 tokens per second decode speed with a time-to-first-token of 13404ms using Q4_K_M quantization.

Can RTX PRO 5000 Blackwell 48GB run Llama 3.3 70B Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on RTX PRO 5000 Blackwell 48GB receives a D grade with 14.4 tok/s and 4K context.

What context window can Llama 3.3 70B Instruct use on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Llama 3.3 70B Instruct can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for Llama 3.3 70B Instruct

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-3-70b-instruct-gguf-on-rtx-pro-5000-blackwell-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: