llmgauge

run it locally

Run Llama 3.3 70B on A100 40GB

70B parameters on a 40 GB card. It fits at Q3_K_M — here's the VRAM math at every quantization, the speed to expect, and the quality you trade.

verdict · Llama 3.3 70B on A100 40GB
Fits at Q3_K_M
needs 36.1 GB at Q3_K_M · 38.0 GB usable · ~44 tokens/sec
Q4_K_M won't fit here — run it in the cloud
Smallest GPU that fits at Q4_K_M: A100 80GB
Rent A100 80GB by the hour
VRAM needed and fit for Llama 3.3 70B on A100 40GB by quantization.
quantization vram needed fits 40gb? tokens/sec quality
FP16 (full) 168.0 GB ✗ no — Reference quality — no quantization loss.
Q8_0 89.0 GB ✗ no — Near-lossless; rarely worth the extra space over Q6.
Q6_K 68.9 GB ✗ no — Virtually indistinguishable from full precision.
Q5_K_M 58.0 GB ✗ no — Minor loss; an excellent quality-vs-size balance.
Q4_K_M 47.0 GB ✗ no — Small but measurable loss; the popular default.
Q3_K_Mrecommended 36.1 GB ✓ yes ~44 Noticeable degradation; only when you're tight on VRAM.

Weights = params × bytes/weight, +20% for KV cache & runtime; usable VRAM is 95% of nameplate. Tokens/sec is a bandwidth ceiling (1555 GB/s) — real throughput is lower with long context. Try other combinations →

Running Llama 3.3 70B on a A100 40GB

At Q3_K_M, Llama 3.3 70B's weights take about 36 GB, inside the A100 40GB's 38 GB of usable memory. Noticeable degradation; only when you're tight on VRAM. Generation speed is bound by memory bandwidth — the GPU reads the whole model once per token — so expect on the order of 44 tokens/sec before context overhead. To run it at the popular Q4_K_M default you'd want a A100 80GB or larger — or rent one by the hour rather than buy.

Quantization is the lever

Each step down in precision shrinks the model: FP16 needs about 168 GB, Q4_K_M about 47 GB — a 72% reduction for a small, usually acceptable quality cost. Q5_K_M and Q6_K are near-lossless if you have the headroom; drop to Q3 only when you're genuinely out of VRAM. The quantization guide covers the tradeoffs in detail.

Frequently asked questions

Can a A100 40GB run Llama 3.3 70B?

Yes — Llama 3.3 70B fits on a A100 40GB at Q3_K_M (36 GB of the 38 GB usable), with roughly 44 tokens/sec. Higher-precision quants need more VRAM; the table above shows each option.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B is a 70B-parameter model. At FP16 that's about 168 GB; at Q4_K_M (the popular default) about 47 GB, including ~20% for the KV cache and runtime. Quantization is the main lever — see the per-quant table above.

Other combinations

Llama 3.3 70B on other GPUs: RTX 3090, RTX 4090, NVIDIA L4, RTX A6000, NVIDIA L40S, A100 80GB

Other models on the A100 40GB: Gemma 2 27B, Qwen2.5 32B, Command R 35B, Mixtral 8x7B, Qwen2.5 72B, Mixtral 8x22B

Related