FitMyLLM
▸ NVIDIA· FERMI 2.0

NVIDIA GeForce GTX 570M

Running LLMs on the GeForce GTX 570M — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
72
GB/S
MODELS Q4
25/449
6%
7B Q4 SPEED
~8
USABLE
▸ MODEL COVERAGE @ Q46% 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
72 GB/s
FP16 COMPUTE
0.8 TFLOPS
TDP
75W
MEMORY
GDDR5
ARCHITECTURE
Fermi 2.0
CUDA CORES
336
25
FAST MODELS · >30 TOK/S
Real-time chat speed
25
USABLE · >10 TOK/S
Comfortable for all tasks
25
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test GeForce GTX 570M (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 25
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
640
TOK/S · 36% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
427
TOK/S · 38% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
411
TOK/S · 38% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
411
TOK/S · 38% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
225
TOK/S · 43% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
213
TOK/S · 44% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
189
TOK/S · 45% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
172
TOK/S · 46% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
172
TOK/S · 46% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
172
TOK/S · 46% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
165
TOK/S · 47% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
160
TOK/S · 47% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
152
TOK/S · 48% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
115
TOK/S · 53% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
115
TOK/S · 53% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
111
TOK/S · 54% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
101
TOK/S · 56% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
101
TOK/S · 56% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
96
TOK/S · 57% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
96
TOK/S · 57% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
96
TOK/S · 57% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
93
TOK/S · 58% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
78
TOK/S · 63% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
77
TOK/S · 63% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
71
TOK/S · 66% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your GeForce GTX 570M.

WHAT THIS CARD IS WORTH

GTX 570M holds 25 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Falcon-H1 1.5B 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
Falcon-H1 1.5B1.55B · Q4_K_M1.3 GB57 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q4_K_M1.3 GB59 tok/sEST
Qwen2 Math 1.5B1.5B · Q4_K_M1.3 GB59 tok/sEST
Qwen 2.5 1.5B1.5B · Q4_K_M1.3 GB59 tok/sEST
Stella en 1.5B v51.5B · Q4_K_M1.3 GB59 tok/sEST
EXAONE-4.0-1.2B1.3B · Q4_K_M1.3 GB68 tok/sEST
MiniCPM-V 4.61.3B · Q5_K_M1.3 GB58 tok/sEST
LFM2.5-1.2B-Thinking1.2B · Q5_K_M1.3 GB63 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
Radeon RX 6600S4 GB$175—
GTX 570M2 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 570M — 2 GB VRAM.

▸ GEFORCE GTX 570M SPEC
BRAND
NVIDIA
VRAM
2 GB GDDR5
BANDWIDTH
72 GB/s
FP16 COMPUTE
0.8 TFLOPS
FP32 COMPUTE
0.8 TFLOPS
CUDA CORES
336
TDP
75 W
ARCHITECTURE
Fermi 2.0
▸ AI CAPABILITY
25/ 449 models @ Q4

With 2 GB VRAM and 72 GB/s bandwidth, this GPU handles models up to 0.81B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~8 tok/s.

§ 01TOP MODELS FOR GEFORCE GTX 570M
25 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 Large 774M0.81B1.0 GB715.6
Qwen3 0.6B0.75B0.9 GB7719.1
LFM2 700M0.74B0.9 GB7850.4
Qwen 1.5 0.5B0.62B0.9 GB939.9
Qwen3-Embedding 0.6B0.6B0.9 GB96—
Falcon-H1R Tiny 0.6B0.6B0.9 GB96—
Falcon Perception 0.6B0.6B0.9 GB96—
BGE-M30.568B0.8 GB10163.0
Snowflake Arctic Embed L v2.00.568B0.8 GB101—
Falcon-H1 0.5B0.52B0.8 GB11141.7
Qwen 2.5 0.5B0.5B0.8 GB11519.4
SmolVLM 500M0.5B0.8 GB115—
GPT-2 Medium 345M0.38B0.7 GB1525.9
SmolLM2 360M0.36B0.7 GB1608.2
LFM2 350M0.35B0.7 GB16546.3
bge-large-en-v1.5 335M0.335B0.7 GB17262.3
mxbai-embed-large-v10.335B0.7 GB17264.7
Snowflake Arctic Embed L0.335B0.7 GB17256.0
Snowflake Arctic Embed M v2.00.305B0.7 GB189—
Gemma 3 270M0.27B0.7 GB21312.6