FitMyLLM
▸ NVIDIA· PASCAL

NVIDIA Tesla P40

Running LLMs on the Tesla P40 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
24 GB
HIGH-END
BANDWIDTH
347
GB/S
MODELS Q4
293/449
65%
7B Q4 SPEED
~40
FAST
▸ MODEL COVERAGE @ Q465% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

Average speeds at Q4 quantization. Actual performance varies by model architecture and context length.

3B
~93
TOK/S
7B
~40
TOK/S
14B
~20
TOK/S
32B
~9
TOK/S
70B
—
39.4GB NEEDED
▸ MEASURED RIG REPORTS

We rent the machine and time every model on it: decode, VRAM peak, concurrency, watts and cost per million tokens. Yours may already be one of them — and two are free to read in full.

SEE THE REPORTS →
▸ SPECIFICATIONS
VRAM
24 GB
BANDWIDTH
347 GB/s
FP16 COMPUTE
0.2 TFLOPS
TDP
250W
MEMORY
GDDR5
ARCHITECTURE
Pascal
CUDA CORES
3,840
PCIE
Gen 3 x16
228
FAST MODELS · >30 TOK/S
Real-time chat speed
290
USABLE · >10 TOK/S
Comfortable for all tasks
293
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying Tesla P40 costs $15–$40k and isn’t practical for most teams. Spin one up by the hour instead:

Rent on Vast.ai →Or RunPod →

Spin up in ~60s. Pay by the second. Cancel anytime.

Some links are affiliate links — we may earn a small commission at no extra cost to you. This helps keep FitMyLLM free and independent.

▸ COMPATIBLE MODELS· 293
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
3084
TOK/S · 2% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
2056
TOK/S · 2% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1983
TOK/S · 2% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1983
TOK/S · 2% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
1084
TOK/S · 3% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
1028
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
910
TOK/S · 3% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
829
TOK/S · 3% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
829
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
829
TOK/S · 3% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
793
TOK/S · 3% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
771
TOK/S · 3% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
731
TOK/S · 3% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
555
TOK/S · 3% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
555
TOK/S · 3% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
534
TOK/S · 3% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
489
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
489
TOK/S · 3% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
463
TOK/S · 4% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
463
TOK/S · 4% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
463
TOK/S · 4% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
448
TOK/S · 4% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
375
TOK/S · 4% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
370
TOK/S · 4% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
343
TOK/S · 4% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
319
TOK/S · 4% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
295
TOK/S · 4% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
257
TOK/S · 5% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
252
TOK/S · 5% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
231
TOK/S · 5% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
231
TOK/S · 5% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
231
TOK/S · 5% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
231
TOK/S · 5% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
214
TOK/S · 5% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
214
TOK/S · 5% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
214
TOK/S · 5% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
206
TOK/S · 5% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
195
TOK/S · 6% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
195
TOK/S · 6% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
185
TOK/S · 23% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Tesla P40.

▸Find models for Tesla P40Compare with other GPUs
WHAT THIS CARD IS WORTH

Tesla P40 holds 293 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is DeepSeek Coder 33B at IQ4_XS.

TOKENS/SEC PER $100
—
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
—
what memory costs on this card
THE BIGGEST IT TAKES
DeepSeek Coder 33B33B · IQ4_XS21.6 GB10 tok/sEST
DeepSeek-R1-Distill-Qwen-32B32.8B · IQ4_XS21.5 GB10 tok/sEST
Qwen3 32B32.8B · IQ4_XS21.5 GB10 tok/sEST
Qwen2.5-32B32.5B · IQ4_XS21.3 GB10 tok/sEST
Qwen 2.5 Coder 32B32.5B · IQ4_XS21.3 GB10 tok/sEST
QwQ-32B32.5B · IQ4_XS21.3 GB10 tok/sEST
Granite Switch 4.1 30B Preview32.24B · IQ4_XS21.1 GB10 tok/sEST
OLMo-2-0325-32B32.2B · IQ4_XS21.1 GB10 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M3 Max (36GB)27 GB$2,4991.5 tok/s per $100
M3 Pro (36GB)27 GB$1,9991.2 tok/s per $100
RTX 509032 GB$1,9997.9 tok/s per $100
RTX 5090 D32 GB$1,9997.9 tok/s per $100
Tesla P4024 GB——

Neighbours in memory rather than in price: memory decides whether a card can do the job at all, so two cards of the same size at different prices is the comparison you are making. Ordered by memory, then bandwidth — not by the value column, which is worked out from bandwidth and price alone and therefore rewards a cheap card whatever its software stack does to that bandwidth in practice. Read it as one input, not as a ranking.

▸ DEVICE UNDER TEST

NVIDIA Tesla P40 — 24 GB VRAM.

▸ TESLA P40 SPEC
BRAND
NVIDIA
VRAM
24 GB GDDR5
BANDWIDTH
347 GB/s
FP16 COMPUTE
0.2 TFLOPS
FP32 COMPUTE
11.8 TFLOPS
CUDA CORES
3,840
TDP
250 W
ARCHITECTURE
Pascal
▸ AI CAPABILITY
293/ 449 models @ Q4

With 24 GB VRAM and 347 GB/s bandwidth, this GPU handles models up to 30.5B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~40 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR TESLA P40
293 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Qwen3 30B A3B30.5B19.1 GB9346.7
Qwen3-Coder 30B-A3B30.5B19.1 GB8436.9
Qwen3-30B-A3B Instruct 250730.5B19.1 GB8443.0
MPT-30B30B18.8 GB926.8
OPT 30B30B18.8 GB96.3
Qwen3-Omni 30B-A3B30B18.8 GB9340.6
Granite 4.1 30B30B18.8 GB924.7
TranslateGemma 27B28.84B18.1 GB1038.6
PaliGemma 2 28B28B17.6 GB1038.6
ERNIE 4.5 VL 28B A3B Thinking28B17.6 GB93—
Qwen3.5-27B27.8B17.5 GB1059.4
Qwen 3.8 27B27.78B17.5 GB1064.6
gemma-3-27b27.4B17.2 GB1027.2
gemma-2-27b27.2B17.1 GB1034.6
Qwen 3.6 27B27B17.0 GB1041.1
Gemma 4 26B A4B26B16.4 GB6947.9
Aria 25B A3.9B25.3B16.0 GB7164.8
Mistral-Small-24B24B15.2 GB1225.0
Mistral-Small-3.1-24B24B15.2 GB1228.8
Magistral Small 24B24B15.2 GB1247.0