FitMyLLM
▸ NVIDIA· KEPLER

NVIDIA GeForce GTX 650 Ti OEM

Running LLMs on the GeForce GTX 650 Ti OEM — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
86
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~10
USABLE
▸ MODEL COVERAGE @ Q410% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
—
1.7GB NEEDED
7B
—
3.9GB NEEDED
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
2 GB
BANDWIDTH
86 GB/s
FP16 COMPUTE
1.4 TFLOPS
TDP
110W
MEMORY
GDDR5
ARCHITECTURE
Kepler
CUDA CORES
768
PCIE
Gen 3 x16
47
FAST MODELS · >30 TOK/S
Real-time chat speed
47
USABLE · >10 TOK/S
Comfortable for all tasks
47
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test GeForce GTX 650 Ti OEM (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 47
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
764
TOK/S · 27% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
510
TOK/S · 29% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
491
TOK/S · 29% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
491
TOK/S · 29% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
269
TOK/S · 32% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
255
TOK/S · 33% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
226
TOK/S · 34% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
205
TOK/S · 35% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
205
TOK/S · 35% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
205
TOK/S · 35% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
197
TOK/S · 35% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
191
TOK/S · 35% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
181
TOK/S · 36% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
138
TOK/S · 40% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
138
TOK/S · 40% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
132
TOK/S · 40% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
121
TOK/S · 42% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
121
TOK/S · 42% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
115
TOK/S · 43% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
115
TOK/S · 43% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
115
TOK/S · 43% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
111
TOK/S · 43% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
93
TOK/S · 47% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
92
TOK/S · 47% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
85
TOK/S · 49% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
79
TOK/S · 51% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
73
TOK/S · 53% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
64
TOK/S · 57% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
63
TOK/S · 58% VRAM
›
A
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
57
TOK/S · 61% VRAM
›
A
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
57
TOK/S · 61% VRAM
›
A
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
57
TOK/S · 61% VRAM
›
A
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
57
TOK/S · 61% VRAM
›
A
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
53
TOK/S · 64% VRAM
›
A
OPT 1.3B1.3B
OPT·2K CTX· CHAT
53
TOK/S · 64% VRAM
›
A
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
53
TOK/S · 64% VRAM
›
A
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
51
TOK/S · 66% VRAM
›
A
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
48
TOK/S · 68% VRAM
›
A
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
48
TOK/S · 68% VRAM
›
A
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
46
TOK/S · 70% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your GeForce GTX 650 Ti OEM.

WHAT THIS CARD IS WORTH

GTX 650 Ti OEM holds 47 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Moondream2 1.9B 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
Moondream2 1.9B1.9B · Q4_K_M1.7 GB56 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB54 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB57 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB51 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB52 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB52 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB52 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB52 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M2 (8GB)5 GB$5992.8 tok/s per $100
M3 (8GB)5 GB$5992.8 tok/s per $100
M1 (8GB)5 GB$4992.2 tok/s per $100
RTX 2060 6GB6 GB$15026.7 tok/s per $100
GTX 650 Ti OEM2 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 GeForce GTX 650 Ti OEM — 2 GB VRAM.

▸ GEFORCE GTX 650 TI OEM SPEC
BRAND
NVIDIA
VRAM
2 GB GDDR5
BANDWIDTH
86 GB/s
FP16 COMPUTE
1.4 TFLOPS
FP32 COMPUTE
1.4 TFLOPS
CUDA CORES
768
TDP
110 W
ARCHITECTURE
Kepler
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 86 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~10 tok/s.

§ 01TOP MODELS FOR GEFORCE GTX 650 TI OEM
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB435.1
stablelm-2-1_6b1.6B1.5 GB439.5
Falcon-H1 1.5B1.55B1.4 GB4443.8
Qwen2.5-Coder-1.5B1.5B1.4 GB4619.6
Qwen2 Math 1.5B1.5B1.4 GB4619.6
Qwen 2.5 1.5B1.5B1.4 GB4630.2
Yi Coder 1.5B1.5B1.4 GB4614.6
Stella en 1.5B v51.5B1.4 GB46—
Phi-1 1.3B1.42B1.4 GB487.2
Phi-1.5 1.3B1.42B1.4 GB487.2
DeepSeek Coder 1.3B1.35B1.3 GB5116.8
EXAONE-4.0-1.2B1.3B1.3 GB5318.9
OPT 1.3B1.3B1.3 GB535.3
MiniCPM-V 4.61.3B1.3 GB5321.5
LFM2.5-1.2B-Thinking1.2B1.2 GB5719.6
Llama-3.2-1B1.2B1.2 GB5710.1
LFM2 1.2B1.2B1.2 GB5715.6
Zamba2 1.2B1.2B1.2 GB5741.5
TinyLlama 1.1B1.1B1.2 GB6313.6
MiniCPM5 1B1.08B1.1 GB6426.5