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URL: https://willitrunai.com/can-run/hf-maziyarpanahi--llama-3-3-70b-instruct-gguf-on-rtx-pro-6000-blackwell-96gb

⇱ Llama 3.3 70B Instruct on RTX PRO 6000 Blackwell Workstatio…

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

YES — Runs Great

C53Usable
○Estimated from fit model

Llama 3.3 70B Instruct needs ~61.7 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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, 35.3 tok/s, Runs well
61.7 GB required96.0 GB available
64% VRAM used

Fit status

Runs well

Decode

35.3 tok/s

TTFT

5492 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 Workstation 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: 35.3 tok/s decode · 5.5s TTFT (warm) · 88 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well35.3 tok/s2996 ms83K
CodingCRuns well35.3 tok/s5492 ms83K
Agentic CodingCRuns well35.3 tok/s7988 ms83K
ReasoningCRuns well35.3 tok/s6490 ms83K
RAGCRuns well35.3 tok/s9985 ms83K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowC42
Q3_K_S
3
34.3 GB
LowC44
NVFP4
4
39.2 GB
MediumC45
Q4_K_M
4
42.7 GB
MediumC46
Q5_K_M
5
50.4 GB
HighC47
Q6_K
6
57.4 GB
HighC48
Q8_0Best for your GPU
8
74.9 GB
Very HighC48
F16
16
143.5 GB
MaximumF0

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 (+3508)
C
Raises estimated decode speed by about 146%.86.9 tok/s decode

Raises estimated decode speed by about 146%.

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

~$12,000 MSRP

Frequently asked questions

See all results for RTX PRO 6000 Blackwell Workstation Edition 96GBSee all hardware for Llama 3.3 70B Instruct