NVIDIA · datacenter

Tesla P40

Tesla P40 has 24 GB of VRAM at 346 GB/s — about 22.32 GiB usable after driver and compositor overhead. 1962 of 2118 indexed models fit at 8K context with q8_0 KV.

Spec sheet· bandwidth, theoreticalFrom the file· fit from summed bytesPredicted· speed
Memory
24 GB
GDDR5
Bandwidth
346 GB/s
384-bit bus
Tensor FP16
dense
TDP
250 W
KV cachef16q8_0q4_0quantizing the KV cache is a ~2× lever on the dominant term at long context
text 1685vision language 173image 2video 16audio tts 21audio asr 39embedding 26

What fits at 8K context

largest quantization that fits, per model · 1962 of 2118 indexed
ModelBest quantParamsWeightsKVTotal in memoryHeadroomtok/s
reka-flash-3.1Q8_020.9B20.69 GiB0.55 GiB22.31 GiB0.01 GiB9±22%
reka-flash-3Q8_020.9B20.69 GiB0.55 GiB22.31 GiB0.01 GiB9±22%
IQuest-Coder-V1-40B-InstructI1-IQ4_XS39.8B19.86 GiB1.33 GiB22.28 GiB0.04 GiB9±22%
Apertus-70B-Instruct-2509IQ2_XS70.6B19.75 GiB1.33 GiB22.26 GiB0.06 GiB9±22%
Skyfall-31B-v4.2Q5_K_S31.4B20.23 GiB0.90 GiB22.25 GiB0.07 GiB9±22%
Wizard-Vicuna-30B-UncensoredI1-Q3_K_M32.5B14.69 GiB6.47 GiB22.24 GiB0.08 GiB9±22%
archangel_sft-kto_llama30bI1-Q3_K_M32.5B14.69 GiB6.47 GiB22.24 GiB0.08 GiB9±22%
Kimi-Linear-48B-A3B-InstructMoEIQ3_M49.1B21.10 GiB0.13 GiB22.23 GiB0.09 GiB9±22%
WizardLM-Uncensored-SuperCOT-StoryTelling-30bQ3_K_M32.5B14.68 GiB6.47 GiB22.22 GiB0.10 GiB9±22%
umt5-xxlF325.7B21.17 GiB0.00 GiB22.22 GiB0.10 GiB9±22%
Pantheon-Reasoning-27BI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwen3.6-27B-uncensored-heretic-v2-Native-MTP-PreservedI1-Q6_K27.4B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTPI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwen3.6-27B-Fable-5-ExperimentalI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwable-5-27B-CoderI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16Q6_K27.4B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
EVE-27b-XENO-HAT-DeepSeek-V4-FlashI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
EVE-27B-XENO-HATI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Godoter-27BI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Reasoning-Medical-27BI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwopus3.6-27B-v2-abliteratedI1-Q6_K27.4B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-BF16I1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Reasoning-Medical0.1-27BI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Huihui-ThinkingCap-Qwen3.6-27B-abliteratedI1-Q6_K27.4B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Semancer-27BI1-Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwen3.6-27B-Uncensored-CyberQ6_K27.4B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwen3.6-27B-Omnimerge-v4Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwopus3.6-27B-v2Q6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Darwin-28B-CoderI1-Q6_K26.9B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Qwopus3.6-27B-CoderQ6_K27.8B20.89 GiB0.27 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-Gembrain-X-Core-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-Gembrain-X-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-31B-Isometry-Fabled-PersonaI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Versipellis-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma4-Gutenberg-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
G4-MeroMero-31B-uncensored-hereticI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-Novelist-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Wanabi-Gemma4-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
G4-Alice-v1.2-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Agares-31B-v1I1-Q5_K_S30.7B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma4-Gutenberg-31B-HereticI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-hereticI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-Gemsicle-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-Gembrain-31B-it-uncensored-hereticI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Melinoe-Gemma4-31B-VL-hereticI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
G4-MeroMero-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Glistening-Gem-31B-v1.0I1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Melinoe-Gemma4-31B-VLI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-31B-Storymaxxed3I1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-AssGuard-31BI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
copywriter-gemma4-31bI1-Q5_K_S32.7B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
gemma-4-31B-heretic-finetuneI1-Q5_K_S30.7B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-Garnet-V2-31B-it-ultra-uncensored-hereticI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
gemma-4-31B-it-abliterated-v3I1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Gemma-4-Harmonia-31B-uncensored-hereticQ5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
gemma-4-31B-it-noloopI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Webs-Sejong-31B-v7I1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
Lilith-31B-v1.0I1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
JGOS-31B-ThinkI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
gemma-4-31B-MergemaxxedI1-Q5_K_S31.3B19.85 GiB1.29 GiB22.22 GiB0.10 GiB9±22%
From the filePredictedwhat these mean

Speed is modeled, not measured: decode is memory-bandwidth bound, so tokens per second is bytes read per token against achievable bandwidth. Mixture-of-experts models carry a wider band because only the routed experts are read each step, and few have been measured publicly.

Measured on this card

third-party benchmarks, aggregated
WorkloadMedianMiddle 50%Runs
Image generation3.02 it/s1.803.45169
Benchmarked· n=169

Aggregated from community-submitted runs, so the spread is wide by nature — it covers different models, resolutions, step counts and settings, not one controlled configuration. Read the middle 50% rather than the median alone. These figures are reproduced with attribution from vladmandic-sd-data-benchmark, which publishes no licence — so we display and link rather than redistribute them.

Questions people ask

What AI models can a Tesla P40 run?
1962 of 2118 indexed open-weight models fit a Tesla P40 at 8,192 context with q8_0 KV cache, the largest being reka-flash-3.1 at Q8_0. That covers text, vision-language, image, video and speech models.
How much usable memory does a Tesla P40 actually have?
Its nameplate is 24 GB, but about 22.32 GiB is available to a model once driver and compositor overhead is accounted for.
Is a Tesla P40 fast for local AI?
Its memory bandwidth is 346 GB/s, and that figure — not teraflops — is what governs token generation speed. Capacity decides what you can run; bandwidth decides how fast it runs.