NVIDIA · Ampere

What runs on a A40

48 GB at 696 GB/s. The largest model that fits at Q4_K_M is Llama 3.1 70B, at roughly 10.5 tokens per second.

Runs comfortably

6.4 GB of 44.3 GB · 15%
044 GB
Weights 4.6 GB
KV cache 1.0 GB
Runtime overhead 0.8 GB

Llama 3.1 8B at Q4_K_M leaves 37.9 GB spare on a A40. There is room to raise the context length or move up a quantisation level.

Generation83.8tok/s
Prompt processing1961tok/s
Max context128Ktokens
KV per 1K tokens0GB

Every quantisation of Llama 3.1 8B on a A40

Highlighted row is the highest quality that still fits at 8K context.

QuantisationbpwWeights TotalVerdictMax ctxtok/s
FP16 / BF16 16.00 15.0 GB 16.8 GB Runs comfortably 128K 28.0
INT8 / W8A8 8.50 7.7 GB 9.5 GB Runs comfortably 128K 52.5
Q8_0 (GGUF) 8.50 7.7 GB 9.5 GB Runs comfortably 128K 52.5
FP8 (E4M3) 8.00 7.5 GB 9.3 GB Runs comfortably 128K 54.0
Q6_K 6.56 6.1 GB 8.0 GB Runs comfortably 128K 64.9
AWQ 4-bit 4.25 5.4 GB 7.2 GB Runs comfortably 128K 72.7
GPTQ 4-bit 4.25 5.4 GB 7.2 GB Runs comfortably 128K 72.7
MXFP4 4.25 5.4 GB 7.2 GB Runs comfortably 128K 72.7
Q5_K_M 5.67 5.3 GB 7.1 GB Runs comfortably 128K 74.0
Q5_K_S 5.52 5.2 GB 7.0 GB Runs comfortably 128K 75.8
Q4_K_M 4.85 4.6 GB 6.4 GB Runs comfortably 128K 83.8
Q4_K_S 4.58 4.4 GB 6.2 GB Runs comfortably 128K 87.5
Q4_0 4.55 4.4 GB 6.2 GB Runs comfortably 128K 88.0
IQ4_XS 4.25 4.1 GB 5.9 GB Runs comfortably 128K 92.5
Q3_K_M 3.91 3.8 GB 5.7 GB Runs comfortably 128K 98.3
IQ3_M 3.70 3.7 GB 5.5 GB Runs comfortably 128K 102
IQ3_XXS 3.06 3.2 GB 5.0 GB Runs comfortably 128K 116
Q2_K 2.63 2.8 GB 4.6 GB Runs comfortably 128K 128
IQ2_XXS 2.06 2.3 GB 4.2 GB Runs comfortably 128K 149
IQ1_M 1.75 2.1 GB 3.9 GB Runs comfortably 128K 163

Models on a A40

Q4_K_M at 8K context. Bandwidth sets the speed; capacity sets the ceiling.

ModelParamsWeightsVerdictMax ctxtok/s
DeepSeek-R1 671B-A37B 671B 379.0 GB Won't fit 2.05
Qwen3 235B-A22B 235.1B 132.8 GB Won't fit 4.03
gpt-oss 120B-A5.1B 116.8B 66.0 GB Won't fit 28.1
Llama 4 Scout 109B-A17B 109B 61.7 GB Won't fit 9.48
Qwen2.5 72B 72.7B 41.2 GB Won't fit 7K 9.08
Llama 3.1 70B 70.5B 40.0 GB Fits, but tight 11K 10.5
Mixtral 8x7B 46.7B 26.4 GB Runs comfortably 32K 46.6
Command R 35B 35.0B 20.1 GB Runs comfortably 19K 17.3
Yi-1.5 34B 34.4B 19.5 GB Runs comfortably 32K 21.2
Qwen3 32B 32.8B 18.6 GB Runs comfortably 99K 22.0
Qwen2.5-Coder 32B 32.8B 18.6 GB Runs comfortably 99K 22.0
DeepSeek-R1-Distill-Qwen 32B 32.8B 18.6 GB Runs comfortably 99K 22.0
Qwen3 30B-A3B 30.5B 17.3 GB Runs comfortably 128K 98.7
Gemma 3 27B 27.4B 15.6 GB Runs comfortably 128K 26.4
Mistral Small 24B 23.6B 13.4 GB Runs comfortably 32K 30.8
gpt-oss 20B-A3.6B 20.9B 11.9 GB Runs comfortably 128K 143
Qwen3 14B 14.8B 8.5 GB Runs comfortably 128K 47.4
Phi-4 14B 14.7B 8.4 GB Runs comfortably 16K 47.0
Mistral NeMo 12B 12.3B 7.0 GB Runs comfortably 128K 56.2
Gemma 3 12B 12.2B 7.0 GB Runs comfortably 128K 56.9
GLM-4 9B 9.4B 5.4 GB Runs comfortably 128K 76.9
Qwen3 8B 8.2B 4.7 GB Runs comfortably 128K 81.1
Llama 3.1 8B 8.0B 4.6 GB Runs comfortably 128K 83.8
DeepSeek-R1-Distill-Llama 8B 8.0B 4.6 GB Runs comfortably 128K 83.8
Qwen2.5 7B 7.6B 4.4 GB Runs comfortably 128K 93.2
Mistral 7B v0.3 7.3B 4.1 GB Runs comfortably 32K 92.7
Gemma 3 4B 4.3B 2.5 GB Runs comfortably 128K 157
Qwen3 4B 4.0B 2.3 GB Runs comfortably 128K 147
Llama 3.2 3B 3.2B 1.8 GB Runs comfortably 128K 185
Llama 3.2 1B 1.2B 0.7 GB Runs comfortably 128K 491

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