Can I Run Llama 4.5 on NVIDIA A100 80GB (PCIe)?
Written by Jakub Rusinowski · Last updated July 21, 2026
Yes, comfortably — Llama 4.5 Scout at Q4_K_M needs 69.4 GB at 8K context (65.8 GB weights + 3.6 GB KV cache/overhead), leaving ~10.6 GB of the NVIDIA A100 80GB (PCIe)'s 80 GB, at ~84 tok/s (est.), with room for up to 32K context.
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NVIDIA A100 80GB (PCIe) Specs
| VRAM | 80 GB |
| Memory Bandwidth | 1935 GB/s |
Llama 4.5 Scout on the NVIDIA A100 80GB (PCIe): VRAM by quantization
| Quant | VRAM needed | Fits 80 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 221.6 GB | ✗ No | — | — |
| Q8_0 | 119.4 GB | ✗ No | — | — |
| Q6_K | 92.9 GB | ✗ No | — | — |
| Q5_K_M | 80.8 GB | ✗ No | — | — |
| Q4_K_M | 69.4 GB | ✓ Yes | 32K | — |
| Q3_K_M | 50 GB | ✓ Yes | 64K | — |
| Q2_K | 39.4 GB | ✓ Yes | 64K | — |
Assumes an 8K-token context with an f16 KV cache, on an estimated architecture — this model publishes no config we can read. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 80 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q4_K_M on the NVIDIA A100 80GB (PCIe): 8K 69.4 GB · 32K 77.6 GB · 128K 110.6 GB ✗. Past 128K it no longer fits 80 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? Llama 4.5 Scout on 64 GB of system RAM, CPU only: runs, but it is tight.
Llama 4.5 Sizes That Fit the NVIDIA A100 80GB (PCIe) (at 8K context)
| Llama 4.5 Scout | Q4_K_M · 69.4 GB at 8K context · ~84 tok/s (est.) |
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FAQ
Llama 4.5 on the NVIDIA A100 80GB (PCIe) — does it fit?
Yes, comfortably — Llama 4.5 Scout at Q4_K_M needs 69.4 GB at 8K context (65.8 GB weights + 3.6 GB KV cache/overhead), leaving ~10.6 GB of the NVIDIA A100 80GB (PCIe)'s 80 GB, at ~84 tok/s (est.), with room for up to 32K context.
Which Llama 4.5 variant fits best on the NVIDIA A100 80GB (PCIe)?
Llama 4.5 Scout at Q4_K_M quantization needs 69.4 GB at 8K context (65.8 GB weights + 3.6 GB KV cache/overhead), estimated ~84 tokens/sec, up to 32K context.
Llama 4.5 on Other GPUs
- Llama 4.5 on NVIDIA H100 80GB (PCIe)
- Llama 4.5 on Apple M2 Max
- Llama 4.5 on Apple M1 Max
- Llama 4.5 on Apple M5 Pro
- Llama 4.5 on NVIDIA RTX PRO 6000 Blackwell
Popular Models on the NVIDIA A100 80GB (PCIe)
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