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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

Parameters (total)
70.60B
VRAM at Q4_K_M (4k ctx)
47.9 GB
Context window
131,072 tokens
Architecture source
HF config.json (verified)

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.

QuantizationBits / weightWeights onlyTotal + KV @4k ctxTotal + 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)

GPUVRAMBandwidthType
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.

GPUVRAMBandwidthType
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.

GPUVRAMBandwidthType
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 SiliconUnified memoryUsable 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.

DeviceMemoryBandwidthType
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.

SystemTypeMemoryUsable for AIBandwidthGPUs
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.

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