FitMyLLM
▸ NVIDIA· MAXWELL

NVIDIA Quadro M620 Mobile

Running LLMs on the Quadro M620 Mobile — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
80.2
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~9
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
80.2 GB/s
FP16 COMPUTE
1 TFLOPS
TDP
30W
MEMORY
GDDR5
ARCHITECTURE
Maxwell
CUDA CORES
512
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 Quadro M620 Mobile (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
713
TOK/S · 27% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
475
TOK/S · 29% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
458
TOK/S · 29% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
458
TOK/S · 29% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
251
TOK/S · 32% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
238
TOK/S · 33% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
210
TOK/S · 34% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
192
TOK/S · 35% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
192
TOK/S · 35% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
192
TOK/S · 35% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
183
TOK/S · 35% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
178
TOK/S · 35% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
169
TOK/S · 36% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
128
TOK/S · 40% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
128
TOK/S · 40% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
123
TOK/S · 40% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
113
TOK/S · 42% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
113
TOK/S · 42% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
107
TOK/S · 43% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
107
TOK/S · 43% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
107
TOK/S · 43% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
103
TOK/S · 43% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
87
TOK/S · 47% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
86
TOK/S · 47% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
79
TOK/S · 49% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
74
TOK/S · 51% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
68
TOK/S · 53% VRAM
›
A
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
59
TOK/S · 57% VRAM
›
A
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
58
TOK/S · 58% VRAM
›
A
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
53
TOK/S · 61% VRAM
›
A
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
53
TOK/S · 61% VRAM
›
A
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
53
TOK/S · 61% VRAM
›
A
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
53
TOK/S · 61% VRAM
›
A
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
49
TOK/S · 64% VRAM
›
A
OPT 1.3B1.3B
OPT·2K CTX· CHAT
49
TOK/S · 64% VRAM
›
A
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
49
TOK/S · 64% VRAM
›
A
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
48
TOK/S · 66% VRAM
›
A
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
45
TOK/S · 68% VRAM
›
A
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
45
TOK/S · 68% VRAM
›
A
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
43
TOK/S · 70% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Quadro M620 Mobile.

WHAT THIS CARD IS WORTH

Quadro M620 Mobile 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 GB52 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB51 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB53 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB47 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB49 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB49 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB49 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB49 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
Quadro M620 Mobile2 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 M620 Mobile — 2 GB VRAM.

▸ QUADRO M620 MOBILE SPEC
BRAND
NVIDIA
VRAM
2 GB GDDR5
BANDWIDTH
80.2 GB/s
FP16 COMPUTE
1 TFLOPS
FP32 COMPUTE
1 TFLOPS
CUDA CORES
512
TDP
30 W
ARCHITECTURE
Maxwell
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 80.2 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~9 tok/s.

§ 01TOP MODELS FOR QUADRO M620 MOBILE
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB405.1
stablelm-2-1_6b1.6B1.5 GB409.5
Falcon-H1 1.5B1.55B1.4 GB4143.8
Qwen2.5-Coder-1.5B1.5B1.4 GB4319.6
Qwen2 Math 1.5B1.5B1.4 GB4319.6
Qwen 2.5 1.5B1.5B1.4 GB4330.2
Yi Coder 1.5B1.5B1.4 GB4314.6
Stella en 1.5B v51.5B1.4 GB43—
Phi-1 1.3B1.42B1.4 GB457.2
Phi-1.5 1.3B1.42B1.4 GB457.2
DeepSeek Coder 1.3B1.35B1.3 GB4816.8
EXAONE-4.0-1.2B1.3B1.3 GB4918.9
OPT 1.3B1.3B1.3 GB495.3
MiniCPM-V 4.61.3B1.3 GB4921.5
LFM2.5-1.2B-Thinking1.2B1.2 GB5319.6
Llama-3.2-1B1.2B1.2 GB5310.1
LFM2 1.2B1.2B1.2 GB5315.6
Zamba2 1.2B1.2B1.2 GB5341.5
TinyLlama 1.1B1.1B1.2 GB5813.6
MiniCPM5 1B1.08B1.1 GB5926.5