Best AI Models for NVIDIA Tesla P40 (24 GB)

Runs 14B and 35B MoE models well; 27–31B fits but is slow (13 tok/s).

24 GB GDDR5346 GB/s12 TFLOPS FP16250 WPascal 2016

Pascal: no Tensor Cores and 1:64-rate FP16 — fine for GGUF/INT8 inference, poor for FP16/exllama. Passive server card; needs added cooling in a desktop.

What LLMs can the Tesla P40 run?

#	Model	Speed	Quant · VRAM
1	Qwen3.6 35B A3BAlibabaHow to run → Q4_K_M · 22.0 GB · 66.3 t/s	66.3 tok/s	Q4_K_M · 22.0 GB
2	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 72.1 t/s	72.1 tok/s	Q4_K_M · 16.5 GB
3	Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB · 84.9 t/s	84.9 tok/s	Q4_K_M · 19.4 GB
4	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 77.9 t/s	77.9 tok/s	Q4_K_M · 13.1 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 108 t/s	108 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.6 35B A3BAlibabaHow to run → Q4_K_M · 22.0 GB · 66.3 t/s	66.3 tok/s	Q4_K_M · 22.0 GB
2	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 72.1 t/s	72.1 tok/s	Q4_K_M · 16.5 GB
3	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 77.9 t/s	77.9 tok/s	Q4_K_M · 13.1 GB
4	Llama 3.1 8B InstructMeta Q4_K_M · 7.9 GB · 42.4 t/s	42.4 tok/s	Q4_K_M · 7.9 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 108 t/s	108 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.6 35B A3BAlibabaHow to run → Q4_K_M · 22.0 GB · 66.3 t/s	66.3 tok/s	Q4_K_M · 22.0 GB
2	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 72.1 t/s	72.1 tok/s	Q4_K_M · 16.5 GB
3	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 35.4 t/s	35.4 tok/s	Q4_K_M · 6.6 GB
4	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 77.9 t/s	77.9 tok/s	Q4_K_M · 13.1 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 108 t/s	108 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.6 35B A3BAlibabaHow to run → Q4_K_M · 22.0 GB · 66.3 t/s	66.3 tok/s	Q4_K_M · 22.0 GB
2	Qwen3.8 27BAlibaba Q4_K_M · 17.9 GB · 12.9 t/s	12.9 tok/s	Q4_K_M · 17.9 GB
3	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 12.9 t/s	12.9 tok/s	Q4_K_M · 17.9 GB
4	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 108 t/s	108 tok/s	Q4_K_M · 19.3 GB
5	GLM 4.7 FlashZ.ai Q4_K_M · 19.9 GB · 64.1 t/s	64.1 tok/s	Q4_K_M · 19.9 GB

#	Model	Speed	Quant · VRAM
1	Gemma 4 26B A4B ITGoogleHow to run → Q4_K_M · 16.5 GB · 72.1 t/s	72.1 tok/s	Q4_K_M · 16.5 GB
2	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 35.4 t/s	35.4 tok/s	Q4_K_M · 6.6 GB
3	Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 69.0 t/s	69.0 tok/s	Q4_K_M · 3.4 GB
4	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 77.9 t/s	77.9 tok/s	Q4_K_M · 13.1 GB
5	LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 112 t/s	112 tok/s	Q4_K_M · 15.0 GB

#	Model	Speed ↓	Quant · VRAM
1	Qwen3 30B A3B Thinking 2507AlibabaHow to run → Q4_K_M · 19.4 GB · 84.9 t/s	84.9 tok/s	Q4_K_M · 19.4 GB
2	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 108 t/s	108 tok/s	Q4_K_M · 19.3 GB
3	Qwen3 30B A3B Instruct 2507Alibaba Q4_K_M · 19.4 GB · 84.9 t/s	84.9 tok/s	Q4_K_M · 19.4 GB
4	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 77.9 t/s	77.9 tok/s	Q4_K_M · 13.1 GB
5	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 72.1 t/s	72.1 tok/s	Q4_K_M · 16.5 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.6 35B A3BAlibabaHow to run → Q4_K_M · 22.0 GB · 66.3 t/s	66.3 tok/s	Q4_K_M · 22.0 GB
2	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 72.1 t/s	72.1 tok/s	Q4_K_M · 16.5 GB
3	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 35.4 t/s	35.4 tok/s	Q4_K_M · 6.6 GB
4	Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 28.0 t/s	28.0 tok/s	Q4_K_M · 10.9 GB
5	MiniCPM V 4.7 35B A3BOpenBMB Q4_K_M · 22.1 GB · 76.4 t/s	76.4 tok/s	Q4_K_M · 22.1 GB

See all 327 models on the Tesla P40 →Best quant that fits · speeds estimated on this card

About the Tesla P40

The Tesla P40 runs 14B and 35B MoE models well. Pascal generation, released 2016.

GenerationPascalreleased 2016

Tesla P40 strengths and weaknesses

Strengths

Runs 35B MoE models: Qwen3.6 35B A3B at 66.3 tok/s in 22 GB

Weaknesses

70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 24 GB here
Slow on large dense models: Qwen3.8 27B at 12.9 tok/s
Slow prompt reading: A 4K-token prompt on Qwen3.6 35B A3B waits 7.3 s for the first token
No FP8 or NVFP4: Pascal predates both; use GGUF, AWQ or GPTQ builds

Tesla P40 tokens per second by model size

Output speed depends on memory bandwidth (346 GB/s). Grey rows need more than 24 GB.

9BQwen3.5 9B · Q4_K_M · 6.6 GB
35.4tok/s
14BQwen3 14B · Q4_K_M · 11.9 GB
23.7tok/s
27–31BQwen3.8 27B · Q4_K_M · 17.9 GB
12.9tok/s
35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB
66.3tok/s
70BLlama 3.3 70B Instruct · not at 4-bit
needs 51.3 GB
120B MoEGPT OSS 120B · not at 4-bit
needs 70.8 GB

Estimated speeds: Q4_K_M, 4K context, one user. How we estimate

All 327 models on the Tesla P40

Featured only

Best quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works

LLM models compatible with NVIDIA Tesla P40 — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
Qwen3.8 27B27.8BVisionFunctions Q4_K_M·12.9 t/s	Q4_K_M	17.9 GB74%	12.9 t/s	262K	B52, Decent
Gemma 4 26B A4B IT25.8BVisionFunctions Q4_K_M·72.1 t/s	Q4_K_M	16.5 GB69%	72.1 t/s	262K	S86, Runs great
NVIDIA Nemotron 3.5 Lightning 30B A3B BF1631.6BChatFunctions Q4_K_M·107.7 t/s	Q4_K_M	19.3 GB81%	107.7 t/s	262K	S91, Runs great
Qwen3.6 35B A3B36.0BVisionFunctions Q4_K_M·66.3 t/s	Q4_K_M	22.0 GB92%	66.3 t/s	262K	A68, Runs well
Muse Glimmer 30B29.8BVision Q4_K_M·12.3 t/s	Q4_K_M	19.6 GB82%	12.3 t/s	131K	B49, Decent
GLM 4.7 Flash31.2BChatFunctions Q4_K_M·64.1 t/s	Q4_K_M	19.9 GB83%	64.1 t/s	203K	A81, Runs well
LFM2.5 24B A2B24.0BChatFunctions Q4_K_M·111.6 t/s	Q4_K_M	15.0 GB63%	111.6 t/s	131K	S94, Runs great
GPT OSS 20B20.9BChatFunctions Q4_K_M·77.9 t/s	Q4_K_M	13.1 GB55%	77.9 t/s	131K	S87, Runs great
Gemma 4 31B IT31.3BVisionFunctions Q4_K_M·11.0 t/s	Q4_K_M	21.7 GB90%	11.0 t/s	262K	C35, Usable
JEV 27B VL27.8BVisionFunctions Q4_K_M·12.9 t/s	Q4_K_M	17.9 GB74%	12.9 t/s	262K	B52, Decent
Ling 3.0 Tiny7.9BChatFunctions Q4_K_M·182.6 t/s	Q4_K_M	5.5 GB23%	182.6 t/s	131K	S99, Runs great
Hy Memory 30B A3B30.1BChatFunctions Q4_K_M·92.4 t/s	Q4_K_M	19.1 GB80%	92.4 t/s	262K	S90, Runs great
MiniCPM V 4.7 35B A3B35.2BVisionFunctions Q4_K_M·76.4 t/s	Q4_K_M	22.1 GB92%	76.4 t/s	262K	A70, Runs well
MiniCPM5 2B2.5BChatFunctions Q4_K_M·118.4 t/s	Q4_K_M	2.5 GB11%	118.4 t/s	131K	S95, Runs great
MiMo V2.6 Distill Qwen 9B9.4BVisionCodeFunctions Q4_K_M·36.2 t/s	Q4_K_M	6.5 GB27%	36.2 t/s	262K	A73, Runs well

Tesla P40 specs, power and PSU

Specs

Architecture: Pascal
VRAM: 24 GB GDDR5
Memory bandwidth: 346.0 GB/s
FP16 (dense): 11.76 TFLOPS
CUDA cores: 3,840
Tensor cores: 0
Compute capability: 6.1
TDP: 250 W
Released: 2016-09-13

Tesla P40 FAQ

What is the largest model the Tesla P40 can run?

Qwen3.6 35B A3B (36.0B) is the largest at 4-bit or better: at Q4_K_M it uses 22.0 GB of 24 GB, and runs at about 66.3 tok/s.

Can the Tesla P40 run 70B models?

No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 24 GB on this card.

How many tokens per second does the Tesla P40 give?

About 66.3 tok/s on Qwen3.6 35B A3B at Q4_K_M, with the first token after 7.3 s on a 4K-token prompt.

What power supply does the Tesla P40 need?

Plan a 500 W PSU (2× the 250 W TDP).

Is 24 GB enough for coding and agents?

Yes for coding: Qwen3.6 35B A3B, the best of the 4 coding models that fit, runs at about 66 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.

Best coding LLMs 24 GB VRAM 48 GB VRAM GPUs with 24 GB or more RTX 5090 RTX 3090 Ti