FitMyLLM
▸ NVIDIA· PASCAL

NVIDIA Quadro P2200

Running LLMs on the Quadro P2200 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
5 GB
BUDGET
BANDWIDTH
200.2
GB/S
MODELS Q4
104/449
23%
7B Q4 SPEED
~23
GOOD
▸ MODEL COVERAGE @ Q423% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~53
TOK/S
7B
~23
TOK/S
14B
—
7.9GB NEEDED
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
5 GB
BANDWIDTH
200.2 GB/s
FP16 COMPUTE
0.1 TFLOPS
TDP
75W
MEMORY
GDDR5X
ARCHITECTURE
Pascal
CUDA CORES
1,280
PCIE
Gen 3 x16
104
FAST MODELS · >30 TOK/S
Real-time chat speed
104
USABLE · >10 TOK/S
Comfortable for all tasks
104
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Quadro P2200 (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 104
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
1780
TOK/S · 11% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1186
TOK/S · 11% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1144
TOK/S · 11% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1144
TOK/S · 11% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
626
TOK/S · 13% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
593
TOK/S · 13% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
525
TOK/S · 13% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
478
TOK/S · 14% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
478
TOK/S · 14% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
478
TOK/S · 14% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
458
TOK/S · 14% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
445
TOK/S · 14% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
421
TOK/S · 14% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
320
TOK/S · 16% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
320
TOK/S · 16% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
308
TOK/S · 16% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
282
TOK/S · 17% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
282
TOK/S · 17% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
267
TOK/S · 17% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
267
TOK/S · 17% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
267
TOK/S · 17% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
258
TOK/S · 17% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
216
TOK/S · 19% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
214
TOK/S · 19% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
198
TOK/S · 20% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
184
TOK/S · 20% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
170
TOK/S · 21% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
148
TOK/S · 23% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
146
TOK/S · 23% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
133
TOK/S · 24% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
133
TOK/S · 24% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
133
TOK/S · 24% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
133
TOK/S · 24% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
123
TOK/S · 26% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
123
TOK/S · 26% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
123
TOK/S · 26% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
119
TOK/S · 26% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
113
TOK/S · 27% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
113
TOK/S · 27% VRAM
›
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
107
TOK/S · 28% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Quadro P2200.

WHAT THIS CARD IS WORTH

Quadro P2200 holds 104 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is ChatGLM2 6B 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
ChatGLM2 6B6.24B · Q4_K_M4.4 GB32 tok/sEST
ChatGLM3 6B6.24B · Q4_K_M4.4 GB32 tok/sEST
Yi-1.5 6B6.06B · Q4_K_M4.4 GB33 tok/sEST
Yi 6B6B · Q4_K_M4.4 GB33 tok/sEST
Gemma 4 E2B5.1B · Q4_K_M4.4 GB39 tok/sEST
Qwen 3.5 4B4.66B · Q4_K_M4.1 GB42 tok/sEST
InternLM2 5B4.5B · Q5_K_M4.1 GB38 tok/sEST
Qwen3-VL 4B Instruct4.44B · Q4_K_M4.4 GB44 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
RTX 3070 Ti8 GB$49912.6 tok/s per $100
RTX 3060 Ti GDDR6X8 GB$39915.8 tok/s per $100
RTX 3070 Ti 8 GB GA1028 GB$59910.5 tok/s per $100
Arc A7508 GB$19910.1 tok/s per $100
Quadro P22005 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 Quadro P2200 — 5 GB VRAM.

▸ QUADRO P2200 SPEC
BRAND
NVIDIA
VRAM
5 GB GDDR5X
BANDWIDTH
200.2 GB/s
FP16 COMPUTE
0.1 TFLOPS
FP32 COMPUTE
3.8 TFLOPS
CUDA CORES
1,280
TDP
75 W
ARCHITECTURE
Pascal
▸ AI CAPABILITY
104/ 449 models @ Q4

With 5 GB VRAM and 200.2 GB/s bandwidth, this GPU handles models up to 4.5B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~23 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR QUADRO P2200
104 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
InternLM2 5B4.5B3.2 GB3647.6
Qwen3-VL 4B Instruct4.44B3.2 GB3626.6
MedGemma 1.5 4B4.3B3.1 GB375.5
Gemma 3 4B4.3B3.1 GB3722.8
Ministral 3 3B Reasoning4.25B3.1 GB38—
Qwen 1.5 4B4B2.9 GB4012.6
Qwen3 4B4B2.9 GB4040.7
Qwen3-4B Instruct 25074B2.9 GB4037.2
Qwen3-Embedding 4B4B2.9 GB40—
Granite Vision 4.1 4B4B2.9 GB40—
Nemotron 3 Nano 4B3.97B2.9 GB4032.0
Ministral 3 3B3.85B2.8 GB4221.4
Phi-3.5 Mini 3.8B3.82B2.8 GB4246.6
phi-3-mini-4k 3.8B3.8B2.8 GB4230.5
Phi-4-mini 3.8B3.8B2.8 GB4249.0
Qwen2.5-VL-3B3.8B2.8 GB4229.9
Jina Embeddings v43.8B2.8 GB42—
Cogito 3B3.61B2.7 GB4422.1
Falcon3-3B3.23B2.5 GB5025.7
granite-4.0-h-micro 3.2B3.2B2.4 GB5018.4