Which GPU can run Llama 3.1 70B?
Model-to-GPU finder — pick a model, get the VRAM math and every tracked card that fits. All numbers computed live from our sourced hardware database; nothing is estimated without a label.
The math for Llama 3.1 70B at Q4_K_M
Weights: 70.6B params × 4.8 bits/weight ÷ 8 = 42.36 GB
+ loading overhead (10%): 4.24 GB
+ KV cache @ 4,096 ctx: 1.34 GB
= 47.94 GB VRAM required
Basis: calculated from verified architecture config (Params 70.6B, 80 layers, 8 KV heads, 128 head dim, 131072 context — HF config.json (NousResearch/Meta-Llama-3.1-70B-Instruct).). GPU VRAM/bandwidth figures are vendor specifications from our database. This tool never guesses throughput — see measured benchmarks for speed.
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GPUs that can run Llama 3.1 70B at Q4_K_M
Grouped by headroom over the 47.9 GB requirement. Every card links to full specs; “Check price” appears only where the product is verified available on Amazon.
Runs it 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 — fits with little headroom
These hold the model but leave little room; 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 (tensor/pipeline parallel)
One card is too small, but a pair at ~90% parallel efficiency covers the requirement. Setup details: multi-GPU guide.
| GPU | Pair VRAM (×2 @90%) | Bandwidth each | Type | |
|---|---|---|---|---|
| AMD Radeon Pro W7800 | 32 GB ×2 ≈ 57.6 GB usable | 576 GB/s | Pro GPU | Check price |
| GeForce RTX 5090 | 32 GB ×2 ≈ 57.6 GB usable | 1792 GB/s | Consumer GPU | Check price |
| NVIDIA RTX 5000 Ada | 32 GB ×2 ≈ 57.6 GB usable | 576 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 4500 Blackwell | 32 GB ×2 ≈ 57.6 GB usable | 896 GB/s | Pro GPU | Check price |
| NVIDIA A100 40GB SXMEOL | 40 GB ×2 ≈ 72.0 GB usable | 1555 GB/s | Data Center GPU |
Apple Silicon options for Llama 3.1 70B
macOS lets Apple Silicon GPUs address about 75% of unified memory. Speed is bandwidth-bound — compare the GB/s column.
| Mac | 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 |
Go deeper
- Full fit page for Llama 3.1 70B — every quantization level, pre-built systems, FAQ.
- VRAM calculator — start from a parameter count instead of a named model.
- Best GPUs for LLM inference — tier-by-tier buying guide.
Methodology: how we source and verify specs. Found a wrong number? File a correction.