FitMyLLM
NVIDIA· PASCAL

NVIDIA P102-101

Running LLMs on the P102-101 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
10 GB
ENTRY-LEVEL
BANDWIDTH
320
GB/S
MODELS Q4
223/449
50%
7B Q4 SPEED
~37
FAST
▸ MODEL COVERAGE @ Q450% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~85
TOK/S
7B
~37
TOK/S
14B
~18
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
10 GB
BANDWIDTH
320 GB/s
FP16 COMPUTE
0.2 TFLOPS
TDP
250W
MEMORY
GDDR5
ARCHITECTURE
Pascal
CUDA CORES
3,200
PCIE
Gen 3 x4
203
FAST MODELS · >30 TOK/S
Real-time chat speed
223
USABLE · >10 TOK/S
Comfortable for all tasks
223
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

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▸ COMPATIBLE MODELS· 223
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
2844
TOK/S · 5% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1896
TOK/S · 6% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1829
TOK/S · 6% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1829
TOK/S · 6% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
1000
TOK/S · 6% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
948
TOK/S · 7% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
839
TOK/S · 7% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
764
TOK/S · 7% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
764
TOK/S · 7% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
764
TOK/S · 7% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
731
TOK/S · 7% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
711
TOK/S · 7% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
674
TOK/S · 7% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
512
TOK/S · 8% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
512
TOK/S · 8% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
492
TOK/S · 8% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
451
TOK/S · 8% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
451
TOK/S · 8% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
427
TOK/S · 9% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
427
TOK/S · 9% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
427
TOK/S · 9% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
413
TOK/S · 9% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
346
TOK/S · 9% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
341
TOK/S · 9% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
316
TOK/S · 10% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
294
TOK/S · 10% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
272
TOK/S · 11% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
237
TOK/S · 11% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
233
TOK/S · 12% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
213
TOK/S · 12% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
213
TOK/S · 12% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
213
TOK/S · 12% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
213
TOK/S · 12% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
197
TOK/S · 13% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
197
TOK/S · 13% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
197
TOK/S · 13% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
190
TOK/S · 13% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
180
TOK/S · 14% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
180
TOK/S · 14% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
171
TOK/S · 56% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your P102-101.

WHAT THIS CARD IS WORTH

P102-101 holds 223 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Mistral-Nemo 12.2B at Q4_K_M.

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
Mistral-Nemo 12.2B12.2B · Q4_K_M9.0 GB23 tok/sEST
Dolly v2 12B12B · Q4_K_S8.7 GB25 tok/sEST
StableLM 2 12B12B · Q4_K_M9.0 GB23 tok/sEST
Falcon2 11B11B · Q4_K_M8.5 GB26 tok/sEST
SOLAR-10.7B10.7B · Q4_K_M8.1 GB26 tok/sEST
Falcon3-10B10.3B · Q5_K_M8.8 GB23 tok/sEST
GLM-4.1V 9B Thinking10.29B · Q4_K_M8.9 GB27 tok/sEST
Bamba 9B v29.78B · Q5_K_M8.3 GB25 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M3 Pro (18GB)12 GB$1,5991.5 tok/s per $100
M1 Pro (16GB)11 GB$9992.9 tok/s per $100
M2 Pro (16GB)11 GB$1,2992.2 tok/s per $100
M4 (16GB)11 GB$4994.0 tok/s per $100
P102-10110 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 P102-101 — 10 GB VRAM.

P102-101 SPEC
BRAND
NVIDIA
VRAM
10 GB GDDR5
BANDWIDTH
320 GB/s
FP16 COMPUTE
0.2 TFLOPS
FP32 COMPUTE
10.7 TFLOPS
CUDA CORES
3,200
TDP
250 W
ARCHITECTURE
Pascal
▸ AI CAPABILITY
223/ 449 models @ Q4

With 10 GB VRAM and 320 GB/s bandwidth, this GPU handles models up to 12.2B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~37 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR P102-101
223 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Mistral-Nemo 12.2B12.2B7.9 GB2122.4
Dolly v2 12B12B7.8 GB216.4
StableLM 2 12B12B7.8 GB2121.3
Falcon2 11B11B7.2 GB2333.2
SOLAR-10.7B10.7B7.0 GB2428.2
Falcon3-10B10.3B6.8 GB2538.2
Bamba 9B v29.78B6.5 GB2626.1
Qwen 3.5 9B9.65B6.4 GB2750.6
RecurrentGemma 9B9.63B6.4 GB2735.0
glm-4-9b9.4B6.2 GB2720.5
MiniCPM-o 4.59.37B6.2 GB27
gemma-2-9b9.2B6.1 GB2830.2
Yi 1.5 9B9B6.0 GB2830.3
Yi Coder 9B9B6.0 GB2835.8
Ministral 3 8B8.92B5.9 GB2925.7
Ministral 3 8B Reasoning8.92B5.9 GB29
NVIDIA-Nemotron-Nano-9B-v28.9B5.9 GB2944.2
InternLM3 8B Instruct8.8B5.9 GB2938.7
Qwen3-VL 8B Instruct8.77B5.8 GB2926.4
MiniCPM-V 4.58.7B5.8 GB2926.1