FitMyLLM
NVIDIA· PASCAL

NVIDIA Tesla P100 PCIe 12 GB

Running LLMs on the Tesla P100 PCIe 12 GB — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
12 GB
ENTRY-LEVEL
BANDWIDTH
549
GB/S
MODELS Q4
248/449
55%
7B Q4 SPEED
~63
BLAZING
▸ MODEL COVERAGE @ Q455% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~146
TOK/S
7B
~63
TOK/S
14B
~31
TOK/S
32B
18.0GB NEEDED
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
12 GB
BANDWIDTH
549 GB/s
FP16 COMPUTE
19.1 TFLOPS
TDP
250W
MEMORY
HBM2
ARCHITECTURE
Pascal
CUDA CORES
3,584
PCIE
Gen 3 x16
248
FAST MODELS · >30 TOK/S
Real-time chat speed
248
USABLE · >10 TOK/S
Comfortable for all tasks
248
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying Tesla P100 PCIe 12 GB costs $15–$40k and isn’t practical for most teams. Spin one up by the hour instead:

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· 248
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
4880
TOK/S · 5% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
3253
TOK/S · 5% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
3137
TOK/S · 5% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
3137
TOK/S · 5% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
1716
TOK/S · 5% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
1627
TOK/S · 5% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1440
TOK/S · 6% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
1311
TOK/S · 6% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
1311
TOK/S · 6% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
1311
TOK/S · 6% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
1255
TOK/S · 6% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
1220
TOK/S · 6% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1156
TOK/S · 6% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
878
TOK/S · 7% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
878
TOK/S · 7% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
845
TOK/S · 7% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
773
TOK/S · 7% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
773
TOK/S · 7% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
732
TOK/S · 7% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
732
TOK/S · 7% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
732
TOK/S · 7% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
708
TOK/S · 7% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
594
TOK/S · 8% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
586
TOK/S · 8% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
542
TOK/S · 8% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
505
TOK/S · 9% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
467
TOK/S · 9% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
407
TOK/S · 10% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
399
TOK/S · 10% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
366
TOK/S · 10% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
366
TOK/S · 10% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
366
TOK/S · 10% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
366
TOK/S · 10% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
338
TOK/S · 11% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
338
TOK/S · 11% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
338
TOK/S · 11% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
325
TOK/S · 11% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
309
TOK/S · 11% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
309
TOK/S · 11% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
293
TOK/S · 46% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Tesla P100 PCIe 12 GB.

WHAT THIS CARD IS WORTH

Tesla P100 PCIe 12 GB holds 248 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Ling-lite 16.8B 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
Ling-lite 16.8B16.8B · IQ4_XS10.7 GB196 tok/sEST
DeepSeek V2 Lite 16B16B · Q4_K_S10.6 GB188 tok/sEST
StarCoder2 15B15.96B · Q4_K_S10.7 GB28 tok/sEST
DeepSeek-Coder-V2-Lite 15.7B15.7B · Q4_K_S10.4 GB188 tok/sEST
DeepSeek R1 Distill Qwen 14B14.8B · Q4_K_S10.4 GB30 tok/sEST
DeepCoder 14B14.8B · Q4_K_S10.4 GB30 tok/sEST
Qwen2.5-Coder-14B14.8B · Q4_K_S10.4 GB30 tok/sEST
Qwen2.5-14B14.8B · Q4_K_S10.4 GB30 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M4 Pro (24GB)16 GB$1,3992.5 tok/s per $100
M4 (24GB)16 GB$6992.9 tok/s per $100
M2 (24GB)16 GB$9991.7 tok/s per $100
M3 (24GB)16 GB$9991.7 tok/s per $100
Tesla P100 PCIe 12 GB12 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 P100 PCIe 12 GB — 12 GB VRAM.

TESLA P100 PCIE 12 GB SPEC
BRAND
NVIDIA
VRAM
12 GB HBM2
BANDWIDTH
549 GB/s
FP16 COMPUTE
19.1 TFLOPS
FP32 COMPUTE
9.5 TFLOPS
CUDA CORES
3,584
TDP
250 W
ARCHITECTURE
Pascal
▸ AI CAPABILITY
248/ 449 models @ Q4

With 12 GB VRAM and 549 GB/s bandwidth, this GPU handles models up to 14.8B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~63 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR TESLA P100 PCIE 12 GB
248 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
DeepSeek R1 Distill Qwen 14B14.8B9.5 GB3043.9
DeepCoder 14B14.8B9.5 GB3038.7
Qwen2.5-Coder-14B14.8B9.5 GB3041.3
Qwen2.5-14B14.8B9.5 GB3041.3
Qwen3 14B14.8B9.5 GB3045.7
phi-4 14B14.66B9.4 GB3033.7
Phi-4-reasoning 14B14.66B9.4 GB3033.7
Phi-4-reasoning-plus 14B14.66B9.4 GB3075.5
Phi-3-medium-14b14B9.0 GB3133.7
Qwen 1.5 14B14B9.0 GB3141.3
Ministral 3 14B Reasoning13.95B9.0 GB31
Baichuan2 13B13B8.4 GB3423.6
Llama 2 13B13B8.4 GB3417.2
CodeLlama 13B13B8.4 GB3419.7
Vicuna 13B13B8.4 GB3411.8
LLaMA 1 13B13B8.4 GB3432.9
OPT 13B13B8.4 GB3435.8
Orca 2 13B13B8.4 GB3425.4
WizardCoder Python 13B13B8.4 GB3460.1
WizardLM 13B13B8.4 GB3419.5