EDGE OSMETA · 70.6B PARAMETERS
Llama 3.3 70B Instruct hardware and VRAM requirements
Meta’s widely deployed 70B dense model, still a standard choice for private enterprise assistants. Llama 3.3 70B Instruct is a dense model with all 70.6B parameters work on every token. At FP8 it needs about 81 GB of GPU memory for one user, and about 122 GB to serve a team of 16 with 8K context each.
Total parameters
70.6B
Active per token
70.6B (dense)
Context length
128K
Released as
BF16
Minimum VRAM
46 GB at INT4
For a team of 16
122 GB at FP8
VRAM needed to run Llama 3.3 70B Instruct
GPU memory by precision, number of concurrent users and context length. Each user needs their own KV cache, so memory grows with users and context.
| Precision | Weights only | 1 user, 8K | 16 users, 8K | 16 users, 32K | LoRA fine-tune |
|---|---|---|---|---|---|
| BF16 Released | 141 GB | 159 GB | 199 GB | 328 GB | 166 GB |
| FP8 Common quantization | 71 GB | 81 GB | 122 GB | 250 GB | 92 GB |
| INT4 Common quantization | 39 GB | 46 GB | 87 GB | 216 GB | 59 GB |
Full fine-tuning in mixed precision needs about 1.14 TB: weights, gradients and optimizer states at roughly 16 bytes per parameter. LoRA trains small adapter layers on a frozen copy of the model instead, so it needs far less.
GPUs that can run Llama 3.3 70B Instruct on-premise
For a team of 16 using Llama 3.3 70B Instruct at FP8 with 8K context each (about 122 GB), smallest fit first.
NVIDIA H200 NVL
141 GB per GPU · 5.3 GB to spare.
AMD Instinct MI300X
192 GB per GPU · 51 GB to spare.
AMD Instinct MI325X
256 GB per GPU · 109 GB to spare.
NVIDIA DGX B200
1.44 TB per system · 1.17 TB to spare.
NVIDIA H100 NVL
94 GB per GPU · Split across 2 GPUs in one server, 48 GB to spare.
NVIDIA L40S
48 GB per GPU · Split across 4 GPUs in one server, 51 GB to spare.
NVIDIA RTX PRO 6000 Blackwell
96 GB per GPU · Split across 2 GPUs in one server, 51 GB to spare.
NVIDIA DGX Spark
128 GB per system · Needs 2 linked systems, 96 GB to spare.
Some of these setups need several GPUs working as one. Buying the GPUs is the easy part. Vanik Edge OS splits the model across them, keeps it running if a server fails and manages it all from one console. See how Edge OS clusters GPU servers
Weighing two of these? Compare AI hardware side by side, for example H100 vs H200, RTX PRO 6000 vs RTX 5090 or DGX Spark vs Mac Studio.
Running Llama 3.3 70B Instruct for your team?
We will size the GPU server for your users and data, and set up Llama 3.3 70B Instruct on it inside your own building. Free, no obligation.
Llama 3.3 70B Instruct at a glance
FROM SIZING TO RUNNING
Sizing the hardware is the easy part.
Getting several GPUs to work as one system for a whole company is where most projects slow down. That is the part Vanik Edge OS does for you.
STEP 01 · THIS TOOL
Size it
Work out the GPU memory your models need and which GPUs or servers can hold them.
STEP 02 · VANIK EDGE OS
Cluster it
Edge OS joins your GPU servers into one cluster, splits a large model across them and keeps it running if a server fails. NVIDIA and AMD servers work side by side.
STEP 03 · VANIK EDGE OS
Run it
Your team gets AI apps with single sign-on and an audit trail, managed from one console, with your data staying inside your building.
Llama 3.3 70B Instruct GPU questions
Is Llama 3.3 70B free?
Yes, for most organizations. The weights are free to download under the Llama 3.3 Community License, which allows commercial use. Companies with more than 700 million monthly active users need a separate license from Meta.
How much VRAM to run Llama 3.3 70B?
Llama 3.3 70B Instruct needs about 81 GB of GPU memory at FP8 for one user with 8K context: 71 GB for the weights plus the KV cache and runtime overhead. Serving 16 people at once with 8K context each needs about 122 GB.
How much RAM do I need for Llama 3.3 70B?
System RAM should be at least as large as the model files so they can be loaded: about 39 GB at INT4, or 71 GB at FP8. For fast responses, though, the model has to fit in GPU memory (VRAM), not just RAM; running from RAM and CPU is many times slower.
How do I run Llama 3.3 70B locally?
To run Llama 3.3 70B Instruct locally you need about 46 GB of GPU memory at INT4 for one user (122 GB for a team of 16), system RAM of at least 39 GB so the weights can be loaded, around 39 GB of fast NVMe storage for the model files, and an inference engine such as vLLM or SGLang. A team of 16 fits on NVIDIA H200 NVL.
How big is Llama 3.3 70B?
Llama 3.3 70B Instruct has 70.6B parameters in total (dense: all of them work on every token). The weights take about 39 GB at INT4.
Is Llama 3.3 70B multimodal?
No. Llama 3.3 70B is a text-only model: it takes text in and produces text out.
Can Llama 3.3 70B run on a single H100 80GB?
Yes, for light use: at INT4 with one user it needs about 46 GB, within the roughly 72 GB usable on an 80 GB H100.
Can I fine-tune Llama 3.3 70B?
LoRA fine-tuning at FP8 needs roughly 92 GB with 4K sequences; it trains small adapter layers on a frozen copy of the model. Full fine-tuning needs about 1.14 TB, which calls for a multi-GPU training cluster.
How are these numbers calculated?
Weights are parameters × bytes per parameter. The KV cache uses 320.0 KB per token per user. We add about 10% of the weights plus 1 GB for runtime overhead and keep 10% of each GPU free. Figures are planning estimates; real usage depends on the inference engine and settings.
VRAM for similar models
Specifications from Hugging Face: meta-llama/Llama-3.3-70B-Instruct. Memory and bandwidth are manufacturer figures; GPU counts are rounded to 1, 2, 4 or 8 per server. All figures are planning estimates. Last checked November 10, 2026.
Run Llama 3.3 70B Instruct inside your own building
Vanik Edge OS runs open-weight models on NVIDIA DGX, AMD Instinct and other GPU servers on your premises, so your data never leaves.