Can Llama 3.3 70B Instruct run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — Runs Great

C52Usable

Estimated from fit model

Llama 3.3 70B Instruct needs ~61.7 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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) — 61.7 GB, 31.4 tok/s, Runs well

61.7 GB required96.0 GB available

64% VRAM used

Fit status

Runs well

Decode

31.4 tok/s

TTFT

6162 ms

Safe context

83K

Memory

61.7 GB / 96.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B Instruct on RTX PRO 6000 Blackwell Server Edition 96GB
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: 31.4 tok/s decode · 6.2s TTFT (warm) · 79 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	31.4 tok/s	3361 ms	83K
Coding	C	Runs well	31.4 tok/s	6162 ms	83K
Agentic Coding	C	Runs well	31.4 tok/s	8963 ms	83K
Reasoning	C	Runs well	31.4 tok/s	7283 ms	83K
RAG	C	Runs well	31.4 tok/s	11204 ms	83K

Quantization options

How Llama 3.3 70B Instruct (70B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	C42
Q3_K_S	3	34.3 GB	Low	C44
NVFP4	4

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

AMD Instinct MI300A 128GBBudget pick

128 GB VRAM (+32)5300 GB/s (+3703)

Raises estimated decode speed by about 177%.86.9 tok/s decode

Raises estimated decode speed by about 177%.

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

~$12,000 MSRP

Frequently asked questions

Can RTX PRO 6000 Blackwell Server Edition 96GB run Llama 3.3 70B Instruct?

Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run Llama 3.3 70B Instruct with a C grade (Runs well). Expected decode speed: 31.4 tok/s.

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 61.7 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 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, Llama 3.3 70B Instruct achieves approximately 31.4 tokens per second decode speed with a time-to-first-token of 6162ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Server Edition 96GB run Llama 3.3 70B Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on RTX PRO 6000 Blackwell Server Edition 96GB receives a C grade with 31.4 tok/s and 83K context.

What context window can Llama 3.3 70B Instruct use on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, Llama 3.3 70B Instruct can safely use up to 83K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX PRO 6000 Blackwell Server Edition 96GB 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-6000-blackwell-server-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: