Can you run Llama 3.3 70B locally?
Llama 3 family · December 2024 · 70.60B parameters · Hugging Face model card
Llama 3.3 70B needs a GPU with 48 GB of VRAM to run locally at Q4_K_M with a 4k-token context, requiring about 47.9 GB of VRAM. Llama 3.3 70B uses the same 70.6-billion-parameter architecture as Llama 3.1 70B, so every VRAM figure and GPU-fit result on this page applies to both models. An 80 GB GPU such as the NVIDIA H100 80GB runs Llama 3.3 70B comfortably.
Minimum: 48 GB VRAM GPU (RTX A6000, RTX 6000 Ada, L40S) at Q4_K_M, 4k context · Recommended: 80 GB VRAM GPU (H100 80GB, A100 80GB) at Q5_K_M–Q6_K, or 2× RTX PRO 6000 Blackwell 96GB
How much VRAM does Llama 3.3 70B need at each quantization?
Llama 3.3 70B needs 47.9 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.
| Quantization | Bits / weight | Weights only | Total + KV @4k ctx | Total + KV @32k ctx |
|---|---|---|---|---|
| Q4_K_M | 4.8 | 42.4 GB | 47.9 GB | 57.3 GB |
| Q5_K_M | 5.7 | 50.3 GB | 56.7 GB | 66.1 GB |
| Q6_K | 6.6 | 58.3 GB | 65.4 GB | 74.8 GB |
| Q8_0 | 8.5 | 75.0 GB | 83.9 GB | 93.3 GB |
| FP16 | 16 | 141.2 GB | 156.7 GB | 166.1 GB |
Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (80 layers, 8 KV heads, 128 head dim). Source: Params 70.6B, 80 layers, 8 KV heads, 128 head dim, 131072 context — HF config.json (unsloth/Llama-3.3-70B-Instruct mirror; identical architecture to Llama 3.1 70B per Meta model card).
Which GPUs can run Llama 3.3 70B locally?
At Q4_K_M with a 4k context, Llama 3.3 70B needs 47.9 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.
Runs Llama 3.3 70B comfortably (25%+ VRAM headroom)
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| H100 SXM | 80 GB | 3350 GB/s | Data Center GPU | |
| NVIDIA A100 80GB SXM | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA H100 PCIe 80GB | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA RTX PRO 6000 Blackwell | 96 GB | 1792 GB/s | Pro GPU | Check price |
| H200 SXM | 141 GB | 4800 GB/s | Data Center GPU | |
| AMD Instinct MI300X | 192 GB | 5300 GB/s | Data Center GPU | |
| NVIDIA B200 | 192 GB | 8000 GB/s | Data Center GPU | |
| AMD Instinct MI325X | 256 GB | 6000 GB/s | Data Center GPU | |
| AMD Instinct MI355X | 288 GB | 8000 GB/s | Data Center GPU | |
| NVIDIA B300 (Blackwell Ultra) | 288 GB | 8000 GB/s | Data Center GPU |
Minimum GPUs that fit Llama 3.3 70B at Q4_K_M
These GPUs hold the model but leave little headroom — keep contexts short.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| AMD Radeon Pro W7900 | 48 GB | 864 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 5000 Blackwell | 48 GB | 1344 GB/s | Pro GPU | |
| RTX 6000 Ada | 48 GB | 960 GB/s | Pro GPU | Check price |
| RTX A6000 | 48 GB | 768 GB/s | Pro GPU | Check price |
Needs 2+ GPUs to run Llama 3.3 70B
One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 47.9 GB requirement. See our multi-GPU guide for setup.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| AMD Radeon Pro W7800 | 32 GB ×2 | 576 GB/s | Pro GPU | Check price |
| GeForce RTX 5090 | 32 GB ×2 | 1792 GB/s | Consumer GPU | Check price |
| NVIDIA RTX 5000 Ada | 32 GB ×2 | 576 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 4500 Blackwell | 32 GB ×2 | 896 GB/s | Pro GPU | Check price |
| NVIDIA A100 40GB SXMEOL | 40 GB ×2 | 1555 GB/s | Data Center GPU |
Can a Mac run Llama 3.3 70B?
Yes — these Apple Silicon machines fit Llama 3.3 70B at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.
| Apple Silicon | Unified memory | Usable by GPU (~75%) | Bandwidth |
|---|---|---|---|
| Apple M4 Pro | 64 GB | 48 GB | 273 GB/s |
| Apple M3 Max | 128 GB | 96 GB | 400 GB/s |
| M4 Max (MacBook Pro) | 128 GB | 96 GB | 546 GB/s |
| M3 Ultra (Mac Studio) | 512 GB | 384 GB | 819 GB/s |
Can Llama 3.3 70B run on mini PCs, Jetson, or NPU devices?
These edge and NPU devices from our database have enough memory for Llama 3.3 70B at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.
| Device | Memory | Bandwidth | Type |
|---|---|---|---|
| Jetson AGX Orin 64GB | 64 GB | 204 GB/s | Edge Compute Device |
| Snapdragon X Elite (X1E-84-100) | 64 GB | 135 GB/s | NPU Chip |
| AMD Ryzen AI 9 HX 370 (Strix Point) | 96 GB | 120 GB/s | NPU Chip |
Which pre-built systems can run Llama 3.3 70B?
These mini PCs and workstations from our database fit Llama 3.3 70B at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.
| System | Type | Memory | Usable for AI | Bandwidth | GPUs |
|---|---|---|---|---|---|
| ASRock NUC BOX-255H (Core Ultra 7 255H) | Mini PC | 96 GB | 48 GB | 102 GB/s | — |
| Apple Mac Studio M2 Ultra (64GB) | Workstation | 64 GB | 48 GB | 800 GB/s | 1 |
| Custom Dual RTX 4090 Training Workstation | Workstation | 48 GB | 48 GB | 1008 GB/s | 2 |
| Dell Precision 7960 Tower (1x RTX 6000 Ada) | Workstation | 48 GB | 48 GB | 960 GB/s | 1 |
| HP Z8 Fury G5 (1x RTX 6000 Ada) | Workstation | 48 GB | 48 GB | 960 GB/s | 1 |
| AMD Ryzen AI Halo Developer Platform (Max+ 395) | Mini PC | 128 GB | 64 GB | 256 GB/s | — |
| ArsenalPC MES2X Dual RTX 5090 AI Workstation | Workstation | 64 GB | 64 GB | — | 2 |
| GMKtec EVO-X2 (Ryzen AI Max+ 395) | Mini PC | 128 GB | 64 GB | 256 GB/s | — |
| Mac mini M4 Max (128GB) | Mini PC | 128 GB | 64 GB | 546 GB/s | — |
| MinisForum MS-S1 MAX (Ryzen AI Max+ 395) | Mini PC | 128 GB | 64 GB | 256 GB/s | — |
| BOXX APEXX 8R (1x RTX PRO 6000 Blackwell) | Workstation | 96 GB | 96 GB | 1792 GB/s | 1 |
| GEEKOM A9 Mega AI Workstation | Workstation | 128 GB | 96 GB | — | 1 |
| NOVATECH RTX PRO 6000 AI Workstation | Workstation | 96 GB | 96 GB | — | 1 |
| System76 Thelio Major (1x RTX PRO 6000 Blackwell) | Workstation | 96 GB | 96 GB | 1792 GB/s | 1 |
| System76 Thelio Major (2x RTX 6000 Ada) | Workstation | 96 GB | 96 GB | 960 GB/s | 2 |
| Lenovo ThinkStation PGX | Workstation | 128 GB | 115.2 GB | 273 GB/s | 1 |
| NVIDIA DGX Spark | Workstation | 128 GB | 115.2 GB | 273 GB/s | 1 |
| BIZON G3000 G2 (4x RTX 5090) | Workstation | 128 GB | 128 GB | 1792 GB/s | 4 |
| Apple Mac Studio M2 Ultra (192GB) | Workstation | 192 GB | 144 GB | 800 GB/s | 1 |
| HP Z8 Fury G5 (4x RTX 6000 Ada) | Workstation | 192 GB | 192 GB | 960 GB/s | 4 |
Frequently asked questions
Does Llama 3.3 70B need the same VRAM as Llama 3.1 70B?
Yes. Llama 3.3 70B has the identical 70.6-billion-parameter architecture, layer count, and attention configuration as Llama 3.1 70B, so the VRAM requirement is the same: about 47.9 GB at Q4_K_M with a 4k context.
Can Llama 3.3 70B run on 24 GB of VRAM?
Not fully. At Q4_K_M the weights alone are 42.36 GB, so a 24 GB GPU such as the RTX 4090 or RTX 3090 must offload most layers to system RAM, which reduces speed to roughly 1–2 tokens per second. A 48 GB professional GPU is the practical minimum.
Is Llama 3.3 70B better than Llama 3.1 70B for local use?
Llama 3.3 70B matches Llama 3.1 405B-level instruction quality per Meta’s announcement while using the same hardware footprint as Llama 3.1 70B, making it the better choice when you can only run one 70B model locally.