FitMyLLM
NVIDIA· AMPERE

NVIDIA GeForce MX570 A

Running LLMs on the GeForce MX570 A — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
96
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~11
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
96 GB/s
FP16 COMPUTE
4.7 TFLOPS
TDP
25W
MEMORY
GDDR6
ARCHITECTURE
Ampere
CUDA CORES
2,048
TENSOR CORES
64
PCIE
Gen 4 x8
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 MX570 A (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
853
TOK/S · 27% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
569
TOK/S · 29% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
549
TOK/S · 29% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
549
TOK/S · 29% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
300
TOK/S · 32% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
284
TOK/S · 33% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
252
TOK/S · 34% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
229
TOK/S · 35% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
229
TOK/S · 35% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
229
TOK/S · 35% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
219
TOK/S · 35% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
213
TOK/S · 35% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
202
TOK/S · 36% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
154
TOK/S · 40% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
154
TOK/S · 40% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
148
TOK/S · 40% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
135
TOK/S · 42% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
135
TOK/S · 42% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
128
TOK/S · 43% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
128
TOK/S · 43% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
128
TOK/S · 43% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
124
TOK/S · 43% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
104
TOK/S · 47% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
102
TOK/S · 47% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
95
TOK/S · 49% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
88
TOK/S · 51% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
82
TOK/S · 53% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
71
TOK/S · 57% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
70
TOK/S · 58% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
64
TOK/S · 61% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
64
TOK/S · 61% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
64
TOK/S · 61% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
64
TOK/S · 61% VRAM
A
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
59
TOK/S · 64% VRAM
A
OPT 1.3B1.3B
OPT·2K CTX· CHAT
59
TOK/S · 64% VRAM
A
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
59
TOK/S · 64% VRAM
A
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
57
TOK/S · 66% VRAM
A
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
54
TOK/S · 68% VRAM
A
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
54
TOK/S · 68% VRAM
A
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
51
TOK/S · 70% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your GeForce MX570 A.

WHAT THIS CARD IS WORTH

MX570 A 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 GB61 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB60 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB62 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB56 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB58 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB58 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB58 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB58 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
MX570 A2 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 MX570 A — 2 GB VRAM.

GEFORCE MX570 A SPEC
BRAND
NVIDIA
VRAM
2 GB GDDR6
BANDWIDTH
96 GB/s
FP16 COMPUTE
4.7 TFLOPS
FP32 COMPUTE
4.7 TFLOPS
CUDA CORES
2,048
TENSOR CORES
64
TDP
25 W
ARCHITECTURE
Ampere
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 96 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~11 tok/s.

§ 01TOP MODELS FOR GEFORCE MX570 A
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB485.1
stablelm-2-1_6b1.6B1.5 GB489.5
Falcon-H1 1.5B1.55B1.4 GB5043.8
Qwen2.5-Coder-1.5B1.5B1.4 GB5119.6
Qwen2 Math 1.5B1.5B1.4 GB5119.6
Qwen 2.5 1.5B1.5B1.4 GB5130.2
Yi Coder 1.5B1.5B1.4 GB5114.6
Stella en 1.5B v51.5B1.4 GB51
Phi-1 1.3B1.42B1.4 GB547.2
Phi-1.5 1.3B1.42B1.4 GB547.2
DeepSeek Coder 1.3B1.35B1.3 GB5716.8
EXAONE-4.0-1.2B1.3B1.3 GB5918.9
OPT 1.3B1.3B1.3 GB595.3
MiniCPM-V 4.61.3B1.3 GB5921.5
LFM2.5-1.2B-Thinking1.2B1.2 GB6419.6
Llama-3.2-1B1.2B1.2 GB6410.1
LFM2 1.2B1.2B1.2 GB6415.6
Zamba2 1.2B1.2B1.2 GB6441.5
TinyLlama 1.1B1.1B1.2 GB7013.6
MiniCPM5 1B1.08B1.1 GB7126.5